Preparation

Clean the environment.

Set locations, and the working directory.

A package-installation function.

Load those packages.

install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

We will create a datestamp and define the Utrecht Science Park Colour Scheme.

# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.

Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.

Methods

We used the Luminex-platform to measure atherosclerotic plaque proteins. Historically, this was done in two experiments:

Experiment 1:

This entails an experiment where also 20+ other interleukins, cyto- and chemokines, and metalloproteinases were measured. Part of these were measured using LUMINEX, some of them were measured using FACS, ELISA, and activity assays. These assays were run according to instructions from the producer in a research setting.

  • variable MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.

Experiment 2:

This entails an experiment where MCP1 was measured in a clinical diagnostic settings on a clinically validated Luminex-platform. - variable MCP1_pg_ml_2015: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.

Loading data

Clinical data

Loading Athero-Express clinical data.

require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDBraw)

Plaque protein data

Loading Athero-Express plaque protein measurements from 2015.

library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")

names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"

head(AEDB_Protein_2015)
NA

Plasma protein data

Loading Athero-Express plasma protein measurements from 2019/2020 as measured using OLINK.

library(openxlsx)
AEDB_PlasmaProtein_OLINK_CVD2raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD2_forR")
AEDB_PlasmaProtein_OLINK_CVD3raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD3_forR")
AEDB_PlasmaProtein_OLINK_CMraw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CM_forR")

AEDB_PlasmaProtein_OLINK_ProteinInfo <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "ProteinInfo")

AEDB_PlasmaProtein_OLINK_CVD2 <- AEDB_PlasmaProtein_OLINK_CVD2raw %>% filter(QC_Warning_CVD2 == "Pass")
AEDB_PlasmaProtein_OLINK_CVD3 <- AEDB_PlasmaProtein_OLINK_CVD3raw %>% filter(QC_Warning_CVD3 == "Pass")
AEDB_PlasmaProtein_OLINK_CM <- AEDB_PlasmaProtein_OLINK_CMraw %>% filter(QC_Warning_CM == "Pass")

table(AEDB_PlasmaProtein_OLINK_CVD2raw$QC_Warning_CVD2)

   Pass Warning 
    690      10 
table(AEDB_PlasmaProtein_OLINK_CVD2$QC_Warning_CVD2)

Pass 
 690 
table(AEDB_PlasmaProtein_OLINK_CVD3raw$QC_Warning_CVD3)

Pass 
 699 
table(AEDB_PlasmaProtein_OLINK_CVD3$QC_Warning_CVD3)

Pass 
 699 
table(AEDB_PlasmaProtein_OLINK_CMraw$QC_Warning_CM)

   Pass Warning 
    691       9 
table(AEDB_PlasmaProtein_OLINK_CM$QC_Warning_CM)

Pass 
 691 
AEDB_PlasmaProtein_OLINK_CVD2$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD2$Order <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Order <- NULL
AEDB_PlasmaProtein_OLINK_CM$Order <- NULL

temp <- merge(AEDB_PlasmaProtein_OLINK_CVD2, AEDB_PlasmaProtein_OLINK_CVD3, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
              sort = FALSE, all.x = TRUE)

AEDB_PlasmaProtein_OLINK <- merge(temp, AEDB_PlasmaProtein_OLINK_CM, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
              sort = FALSE, all.x = TRUE)

AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_cardiometabolic_plt1_29-10-19"] <- "plate 1"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt2"] <- "plate 2"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt3"] <- "plate 3"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt4"] <- "plate 4"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt5"] <- "plate 5"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_pl6"] <- "plate 6"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_CM_plt10"] <- "plate 10"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_plt11_CM"] <- "plate 11"

olink_proteins <- c("BMP6", "ANGPT1", "ADM", "CD40L", "SLAMF7", "PGF", "ADAMTS13", "BOC", "IL4RA", "SRC", "IL1ra", "IL6", "TNFRSF10A", "STK4", "IDUA", 
                    "TNFRSF11A", "PAR1", "TRAILR2", "PRSS27", "TIE2", "TF", "IL1RL2", "PDGF_subunit_B", "IL27", "IL17D", "CXCL1", "LOX1", "Gal9", "GIF", "SCF", 
                    "IL18", "FGF21", "PIgR", "RAGE", "SOD2", "CTRC", "FGF23", "SPON2", "GH", "FS", "GLO1", "CD84", "PAPPA", "SERPINA12", "REN", "DECR1", 
                    "MERTK", "KIM1", "THBS2", "TM", "VSIG2", "AMBP", "PRELP", "HO1", "XCL1", "IL16", "SORT1", "CEACAM8", "PTX3", "PSGL1", "CCL17", "CCL3", 
                    "MMP7", "IgG_Fc_receptor_IIb", "ITGB1BP2", "DCN", "Dkk1", "LPL", "PRSS8", "AGRP", "HBEGF", "GDF2", "FABP2", "THPO", "MARCO", "GT", "BNP", 
                    "MMP12", "ACE2", "PDL2", "CTSL1", "hOSCAR", "TNFRSF13B", "TGM2", "LEP", "CA5A", "HSP_27", "CD4", "NEMO", "VEGFD", "PARP1", "HAOX1", 
                    "TNFRSF14", "LDL_receptor", "ITGB2", "IL17RA", "TNFR2", "MMP9", "EPHB4", "IL2RA", "OPG", "ALCAM", "TFF3", "SELP", "CSTB", "MCP1", "CD163", 
                    "Gal3", "GRN", "NTproBNP", "BLM_hydrolase", "PLC", "LTBR", "Notch_3", "TIMP4", "CNTN1", "CDH5", "TLT2", "FABP4", "TFPI", "PAI", "CCL24", 
                    "TR", "TNFRSF10C", "GDF15", "SELE", "AZU1", "DLK1", "SPON1", "MPO", "CXCL16", "IL6RA", "RETN", "IGFBP1", "CHIT1", "TRAP", "GP6", "PSPD", 
                    "PI3", "EpCAM", "APN", "AXL", "IL1RT1", "MMP2", "FAS", "MB", "TNFSF13B", "PRTN3", "PCSK9", "UPAR", "OPN", "CTSD", "PGLYRP1", "CPA1", "JAMA", 
                    "Gal4", "IL1RT2", "SHPS1", "CCL15", "CASP3", "uPA", "CPB1", "CHI3L1", "ST2", "tPA", "SCGB3A2", "EGFR", "IGFBP7", "CD93", "IL18BP", "COL1A1", 
                    "PON3", "CTSZ", "MMP3", "RARRES2", "ICAM2", "KLK6", "PDGF_subunit_A", "TNFR1", "IGFBP2", "vWF", "PECAM1", "MEPE", "CCL16", "PRCP", "CA1", 
                    "ICAM1", "CHL1", "TGFBI", "ENG", "PLTP", "SERPINA7", "IGFBP3", "CR2", "SERPINA5", "FCGR3B", "IGFBP6", "CDH1", "CCL5", "CCL14", "GNLY", 
                    "NOTCH1", "PAM", "PROC", "CST3", "NCAM1", "PCOLCE", "LILRB1", "MET", "LTBP2", "IL7R", "VCAM1", "SELL", "F11", "COMP", "CA4", "PTPRS", 
                    "MBL2", "TIMP1", "ANGPTL3", "REG3A", "SOD1", "CD46", "ITGAM", "TNC", "NID1", "CFHR5", "SPARCL1", "PLXNB2", "MEGF9", "ANG", "ST6GAL1", 
                    "DPP4", "REG1A", "QPCT", "FCN2", "FETUB", "CES1", "CRTAC1", "TCN2", "PRSS2", "ICAM3", "SAA4", "CNDP1", "FCGR2A", "NRP1", "EFEMP1", "TIMD4", 
                    "FAP", "TIE1", "THBS4", "F7", "GP1BA", "LYVE1", "CA3", "TGFBR3", "DEFA1", "CD59", "APOM", "OSMR", "LILRB2", "UMOD", "CCL18", "COL18A1", 
                    "LCN2", "KIT", "C1QTNF1", "AOC3", "GAS6", "IGLC2", "PLA2G7", "TNXB", "MFAP5", "VASN", "LILRB5", "C2")

length(olink_proteins)
[1] 276
olink_proteins_rank = unlist(lapply(olink_proteins, paste0, "_rankNorm"))

olink_proteins_short <- c("MCP1")
olink_proteins_short_rank <- unlist(lapply(olink_proteins_short, paste0, "_rankNorm"))

rm(temp)

Merging protein data

We will merge these measurements to the AEDB for comparing pg/ug vs. pg/mL measurements of MCP1 - also in relation to plaque phenotypes. In addition we have more information the experiment and can correct for this.

names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"

temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))

temp2 <- subset(AEDB_PlasmaProtein_OLINK, select = c("STUDY_NUMBER", "MCP1", "MCP1_rankNorm", "Plate_ID"))
names(temp2)[names(temp2) == "MCP1"] <- "MCP1_plasma_olink"
names(temp2)[names(temp2) == "MCP1_rankNorm"] <- "MCP1_plasma_olink_rankNorm"
names(temp2)[names(temp2) == "Plate_ID"] <- "PlateID_plasma_olink"


AEDBraw2 <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)

AEDB <- merge(AEDBraw2, temp2, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)
rm(temp, temp2, AEDBraw2)

temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015",
                                "MCP1_plasma_olink", "MCP1_plasma_olink_rankNorm", "PlateID_plasma_olink"))
dim(temp)
[1] 3793    8
head(temp)
rm(temp)   

Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.

Note: There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.

AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
yearpsy5 [326], yearchol3 [347], yearablo3 [419]

Fixing and creating variables

We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.

Coding of symptoms is as follows:

  • missing -999
  • Asymptomatic 0
  • TIA 1
  • minor stroke 2
  • Major stroke 3
  • Amaurosis fugax 4
  • Four vessel disease 5
  • Vertebrobasilary TIA 7
  • Retinal infarction 8
  • Symptomatic, but aspecific symtoms 9
  • Contralateral symptomatic occlusion 10
  • retinal infarction 11
  • armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
  • retinal infarction + TIAs 13
  • Ocular ischemic syndrome 14
  • ischemisch glaucoom 15
  • subclavian steal syndrome 16
  • TGA 17

We will group as follows in Symptoms.5G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13
  3. Stroke > 2, 3
  4. Ocular > 4, 14, 15
  5. Retinal infarction > 8, 11
  6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3
  3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt2G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
              
               Asymptomatic Ocular Other Retinal infarction Stroke  TIA <NA>
  Asymptomatic          333      0     0                  0      0    0    0
  Symptomatic             0    417   119                 43    733 1045    0
  <NA>                    0      0     0                  0      0    0 1103
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

We will also fix the plaquephenotypes variable.

Coding of symptoms is as follows:

  • missing -999
  • not relevant -888
  • fibrous 1
  • fibroatheromatous 2
  • atheromatous 3

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

     atheromatous fibroatheromatous           fibrous 
              550               843              1439 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

   0    1 
2766  985 
table(AEDB$DiabetesStatus)

Control (no Diabetes Dx/Med)                     Diabetes 
                        2766                          985 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.

  • diet801: are you a smoker?
  • diet802: did you smoke in the past?

We already have some variables indicating smoking status:

  • SmokingReported: patient has reported to smoke.
  • SmokingYearOR: smoking in the year of surgery?
  • SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")

Fixing smoking status.
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")

* Current smoking status.
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))
Current smoker
no data available/missing                        no                       yes                      <NA> 
                        0                      2364                      1310                       119 
cat("\n* Updated smoking status.\n")

* Updated smoking status.
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))
Updated smoking status
Current smoker      Ex-smoker   Never smoked           <NA> 
          1310           1814            389            280 
cat("\n* Comparing to 'SmokerCurrent'.\n")

* Comparing to 'SmokerCurrent'.
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))
                      Current smoker
Updated smoking status no data available/missing   no  yes <NA>
        Current smoker                         0    0 1310    0
        Ex-smoker                              0 1814    0    0
        Never smoked                           0  389    0    0
        <NA>                                   0  161    0  119
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the alcohol status variable.


# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

  No  Yes 
1238 2346 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv


# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)

   0    1 
2432 1285 
table(AEDB$Stroke_history)

   0    1 
2764  948 
table(AEDB$Peripheral.interv)

   0    1 
2581 1099 
table(AEDB$MedHx_CVD)

  No  yes 
1310 2476 
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

Athero-Express Biobank Study

Baseline characteristics

We are interested in the following variables at baseline.

  • Age (years)
  • Female sex (N, %)
  • Hypertension (N, %)
  • SBP (mmHg)
  • DBP (mmHg)
  • Diabetes mellitus (N, %)
  • Total cholesterol levels (mg/dL)
  • LDL cholesterol levels (mg/dL)
  • HDL cholesterol levels (mg/dL)
  • Triglyceride levels (mg/dL)
  • Use of statins (N, %)
  • Use of antiplatelet drugs (N, %)
  • BMI (kg/m²)
  • Smoking status (N, %)
    • Never smokers
    • Ex-smokers
    • Current smokers
  • History of CAD (N, %)
  • History of PAD (N, %)
  • Clinical manifestations
    • Asymptomatic
    • Amaurosis fugax
    • TIA
    • Stroke
  • eGFR (mL/min/1.73 m²)
  • MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments MCP1, and MCP1_pg_ml_2015)
cat("===========================================================================================\n")
===========================================================================================
cat("CREATE BASELINE TABLE\n")
CREATE BASELINE TABLE
# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6R_pg_ml_2015",
                   "MCP1", "MCP1_pg_ml_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

All patients

Showing the baseline table of the whole Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 3793                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     45.7 (1735)     0.0   
                                       UMC Utrecht                                                                  54.3 (2058)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          2.5 (  94)           
                                       2003                                                                          5.4 ( 204)           
                                       2004                                                                          7.6 ( 289)           
                                       2005                                                                          8.1 ( 309)           
                                       2006                                                                          7.5 ( 285)           
                                       2007                                                                          6.2 ( 234)           
                                       2008                                                                          5.9 ( 223)           
                                       2009                                                                          7.1 ( 268)           
                                       2010                                                                          8.1 ( 307)           
                                       2011                                                                          7.1 ( 270)           
                                       2012                                                                          8.2 ( 312)           
                                       2013                                                                          6.9 ( 262)           
                                       2014                                                                          7.9 ( 299)           
                                       2015                                                                          2.1 (  79)           
                                       2016                                                                          3.3 ( 124)           
                                       2017                                                                          2.2 (  85)           
                                       2018                                                                          2.1 (  80)           
                                       2019                                                                          1.8 (  69)           
  Age (mean (SD))                                                                                                 68.906 (9.322)    0.0   
  Gender % (freq)                      female                                                                       30.6 (1161)     0.0   
                                       male                                                                         69.4 (2632)           
  TC_finalCU (mean (SD))                                                                                         185.256 (81.509)  46.8   
  LDL_finalCU (mean (SD))                                                                                        106.533 (40.725)  54.5   
  HDL_finalCU (mean (SD))                                                                                         46.591 (16.725)  51.1   
  TG_finalCU (mean (SD))                                                                                         154.212 (99.774)  51.8   
  TC_final (mean (SD))                                                                                             4.798 (2.111)   46.8   
  LDL_final (mean (SD))                                                                                            2.759 (1.055)   54.5   
  HDL_final (mean (SD))                                                                                            1.207 (0.433)   51.1   
  TG_final (mean (SD))                                                                                             1.743 (1.127)   51.8   
  hsCRP_plasma (mean (SD))                                                                                        19.231 (206.750) 60.6   
  systolic (mean (SD))                                                                                           150.901 (25.114)  13.5   
  diastoli (mean (SD))                                                                                            79.933 (21.847)  13.5   
  GFR_MDRD (mean (SD))                                                                                            74.844 (24.740)   6.5   
  BMI (mean (SD))                                                                                                 26.336 (4.050)    7.5   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     6.6   
                                       Normal kidney function                                                       22.1 ( 839)           
                                       CKD 2 (Mild)                                                                 47.2 (1789)           
                                       CKD 3 (Moderate)                                                             21.9 ( 831)           
                                       CKD 4 (Severe)                                                                1.4 (  53)           
                                       CKD 5 (Failure)                                                               0.8 (  32)           
                                       <NA>                                                                          6.6 ( 249)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     7.5   
                                       Underweight                                                                   1.2 (  44)           
                                       Normal                                                                       35.2 (1336)           
                                       Overweight                                                                   42.1 (1595)           
                                       Obese                                                                        14.1 ( 533)           
                                       <NA>                                                                          7.5 ( 285)           
  SmokerStatus % (freq)                Current smoker                                                               34.5 (1310)     7.4   
                                       Ex-smoker                                                                    47.8 (1814)           
                                       Never smoked                                                                 10.3 ( 389)           
                                       <NA>                                                                          7.4 ( 280)           
  AlcoholUse % (freq)                  No                                                                           32.6 (1238)     5.5   
                                       Yes                                                                          61.9 (2346)           
                                       <NA>                                                                          5.5 ( 209)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 72.9 (2766)     1.1   
                                       Diabetes                                                                     26.0 ( 985)           
                                       <NA>                                                                          1.1 (  42)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     4.0   
                                       no                                                                           23.7 ( 900)           
                                       yes                                                                          72.3 (2742)           
                                       <NA>                                                                          4.0 ( 151)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     5.5   
                                       no                                                                           28.6 (1086)           
                                       yes                                                                          65.9 (2500)           
                                       <NA>                                                                          5.5 ( 207)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     1.3   
                                       no                                                                           13.3 ( 505)           
                                       yes                                                                          85.4 (3240)           
                                       <NA>                                                                          1.3 (  48)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           21.0 ( 798)           
                                       yes                                                                          77.5 (2940)           
                                       <NA>                                                                          1.5 (  55)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           85.6 (3248)           
                                       yes                                                                          12.8 ( 485)           
                                       <NA>                                                                          1.6 (  60)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           13.7 ( 521)           
                                       yes                                                                          84.7 (3213)           
                                       <NA>                                                                          1.6 (  59)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           21.8 ( 826)           
                                       yes                                                                          76.7 (2911)           
                                       <NA>                                                                          1.5 (  56)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     8.1   
                                       No stroke diagnosed                                                          74.4 (2823)           
                                       Stroke diagnosed                                                             17.5 ( 663)           
                                       <NA>                                                                          8.1 ( 307)           
  sympt % (freq)                       missing                                                                      29.1 (1103)     0.0   
                                       Asymptomatic                                                                  8.8 ( 333)           
                                       TIA                                                                          27.4 (1040)           
                                       minor stroke                                                                 12.1 ( 458)           
                                       Major stroke                                                                  7.3 ( 275)           
                                       Amaurosis fugax                                                              10.5 ( 399)           
                                       Four vessel disease                                                           1.1 (  43)           
                                       Vertebrobasilary TIA                                                          0.1 (   5)           
                                       Retinal infarction                                                            1.0 (  37)           
                                       Symptomatic, but aspecific symtoms                                            1.6 (  61)           
                                       Contralateral symptomatic occlusion                                           0.3 (  12)           
                                       retinal infarction                                                            0.2 (   6)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      0.5 (  18)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Ocular                                                                       11.0 ( 417)           
                                       Other                                                                         3.1 ( 119)           
                                       Retinal infarction                                                            1.1 (  43)           
                                       Stroke                                                                       19.3 ( 733)           
                                       TIA                                                                          27.6 (1045)           
                                       <NA>                                                                         29.1 (1103)           
  AsymptSympt % (freq)                 Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Ocular and others                                                            15.3 ( 579)           
                                       Symptomatic                                                                  46.9 (1778)           
                                       <NA>                                                                         29.1 (1103)           
  AsymptSympt2G % (freq)               Asymptomatic                                                                  8.8 ( 333)    29.1   
                                       Symptomatic                                                                  62.1 (2357)           
                                       <NA>                                                                         29.1 (1103)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     4.0   
                                       de novo                                                                      87.0 (3299)           
                                       restenosis                                                                    8.8 ( 334)           
                                       stenose bij angioseal na PTCA                                                 0.2 (   7)           
                                       <NA>                                                                          4.0 ( 153)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     7.0   
                                       0-49%                                                                         0.7 (  25)           
                                       50-70%                                                                        6.8 ( 257)           
                                       70-90%                                                                       35.6 (1349)           
                                       90-99%                                                                       29.9 (1133)           
                                       100% (Occlusion)                                                             14.8 ( 560)           
                                       NA                                                                            0.1 (   3)           
                                       50-99%                                                                        2.6 (  99)           
                                       70-99%                                                                        2.6 ( 100)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          7.0 ( 265)           
  MedHx_CVD % (freq)                   No                                                                           34.5 (1310)     0.2   
                                       yes                                                                          65.3 (2476)           
                                       <NA>                                                                          0.2 (   7)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     2.0   
                                       No history CAD                                                               64.1 (2432)           
                                       History CAD                                                                  33.9 (1285)           
                                       <NA>                                                                          2.0 (  76)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     1.6   
                                       no                                                                           55.1 (2090)           
                                       yes                                                                          43.3 (1644)           
                                       <NA>                                                                          1.6 (  59)           
  Peripheral.interv % (freq)           no                                                                           68.0 (2581)     3.0   
                                       yes                                                                          29.0 (1099)           
                                       <NA>                                                                          3.0 ( 113)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     7.3   
                                       No composite endpoints                                                       60.6 (2299)           
                                       Composite endpoints                                                          32.1 (1218)           
                                       <NA>                                                                          7.3 ( 276)           
  EP_composite_time (mean (SD))                                                                                    2.267 (1.203)    7.4   
  macmean0 (mean (SD))                                                                                             0.656 (1.154)   32.4   
  smcmean0 (mean (SD))                                                                                             2.292 (6.618)   32.4   
  Macrophages.bin % (freq)             no/minor                                                                     42.3 (1603)    25.7   
                                       moderate/heavy                                                               32.1 (1216)           
                                       <NA>                                                                         25.7 ( 974)           
  SMC.bin % (freq)                     no/minor                                                                     22.9 ( 870)    25.3   
                                       moderate/heavy                                                               51.8 (1964)           
                                       <NA>                                                                         25.3 ( 959)           
  neutrophils (mean (SD))                                                                                        162.985 (490.469) 91.0   
  Mast_cells_plaque (mean (SD))                                                                                  165.663 (163.421) 93.0   
  IPH.bin % (freq)                     no                                                                           32.3 (1225)    24.8   
                                       yes                                                                          42.9 (1628)           
                                       <NA>                                                                         24.8 ( 940)           
  vessel_density_averaged (mean (SD))                                                                              8.030 (6.344)   48.0   
  Calc.bin % (freq)                    no/minor                                                                     37.9 (1438)    24.7   
                                       moderate/heavy                                                               37.4 (1417)           
                                       <NA>                                                                         24.7 ( 938)           
  Collagen.bin % (freq)                no/minor                                                                     14.2 ( 540)    25.2   
                                       moderate/heavy                                                               60.6 (2299)           
                                       <NA>                                                                         25.2 ( 954)           
  Fat.bin_10 % (freq)                   <10%                                                                        32.3 (1226)    24.7   
                                        >10%                                                                        43.0 (1630)           
                                       <NA>                                                                         24.7 ( 937)           
  Fat.bin_40 % (freq)                  <40%                                                                         60.0 (2276)    24.7   
                                       >40%                                                                         15.3 ( 580)           
                                       <NA>                                                                         24.7 ( 937)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 14.5 ( 550)    25.3   
                                       fibroatheromatous                                                            22.2 ( 843)           
                                       fibrous                                                                      37.9 (1439)           
                                       <NA>                                                                         25.3 ( 961)           
  IL6 (mean (SD))                                                                                                 94.451 (278.490) 84.5   
  IL6R_pg_ml_2015 (mean (SD))                                                                                    219.949 (252.513) 67.0   
  MCP1 (mean (SD))                                                                                               130.926 (118.422) 83.7   
  MCP1_pg_ml_2015 (mean (SD))                                                                                    587.541 (843.110) 65.3   

CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
                                      
                                       level                                                                     Overall           Missing
  n                                                                                                                 2423                  
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     39.1 ( 948)     0.0   
                                       UMC Utrecht                                                                  60.9 (1475)           
  ORyear % (freq)                      No data available/missing                                                     0.0 (   0)     0.0   
                                       2002                                                                          3.3 (  81)           
                                       2003                                                                          6.5 ( 157)           
                                       2004                                                                          7.8 ( 190)           
                                       2005                                                                          7.6 ( 185)           
                                       2006                                                                          7.6 ( 183)           
                                       2007                                                                          6.3 ( 152)           
                                       2008                                                                          5.7 ( 138)           
                                       2009                                                                          7.5 ( 182)           
                                       2010                                                                          6.6 ( 159)           
                                       2011                                                                          6.8 ( 164)           
                                       2012                                                                          7.3 ( 176)           
                                       2013                                                                          6.1 ( 149)           
                                       2014                                                                          6.7 ( 163)           
                                       2015                                                                          3.1 (  76)           
                                       2016                                                                          3.5 (  85)           
                                       2017                                                                          2.7 (  65)           
                                       2018                                                                          2.7 (  66)           
                                       2019                                                                          2.1 (  52)           
  Age (mean (SD))                                                                                                 69.103 (9.302)    0.0   
  Gender % (freq)                      female                                                                       30.5 ( 739)     0.0   
                                       male                                                                         69.5 (1684)           
  TC_finalCU (mean (SD))                                                                                         184.852 (56.275)  38.0   
  LDL_finalCU (mean (SD))                                                                                        108.484 (41.794)  45.6   
  HDL_finalCU (mean (SD))                                                                                         46.432 (16.999)  41.7   
  TG_finalCU (mean (SD))                                                                                         151.189 (91.249)  42.8   
  TC_final (mean (SD))                                                                                             4.788 (1.458)   38.0   
  LDL_final (mean (SD))                                                                                            2.810 (1.082)   45.6   
  HDL_final (mean (SD))                                                                                            1.203 (0.440)   41.7   
  TG_final (mean (SD))                                                                                             1.708 (1.031)   42.8   
  hsCRP_plasma (mean (SD))                                                                                        19.887 (231.453) 52.9   
  systolic (mean (SD))                                                                                           152.408 (25.163)  11.3   
  diastoli (mean (SD))                                                                                            81.314 (25.178)  11.3   
  GFR_MDRD (mean (SD))                                                                                            73.115 (21.145)   5.4   
  BMI (mean (SD))                                                                                                 26.488 (3.976)    5.9   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (   0)     5.4   
                                       Normal kidney function                                                       19.1 ( 462)           
                                       CKD 2 (Mild)                                                                 50.9 (1233)           
                                       CKD 3 (Moderate)                                                             22.9 ( 554)           
                                       CKD 4 (Severe)                                                                1.3 (  32)           
                                       CKD 5 (Failure)                                                               0.4 (  10)           
                                       <NA>                                                                          5.4 ( 132)           
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (   0)     5.9   
                                       Underweight                                                                   1.0 (  24)           
                                       Normal                                                                       35.1 ( 851)           
                                       Overweight                                                                   43.4 (1052)           
                                       Obese                                                                        14.5 ( 352)           
                                       <NA>                                                                          5.9 ( 144)           
  SmokerStatus % (freq)                Current smoker                                                               33.2 ( 805)     5.9   
                                       Ex-smoker                                                                    48.0 (1163)           
                                       Never smoked                                                                 12.9 ( 313)           
                                       <NA>                                                                          5.9 ( 142)           
  AlcoholUse % (freq)                  No                                                                           34.5 ( 835)     4.1   
                                       Yes                                                                          61.5 (1489)           
                                       <NA>                                                                          4.1 (  99)           
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 75.2 (1822)     1.1   
                                       Diabetes                                                                     23.7 ( 574)           
                                       <NA>                                                                          1.1 (  27)           
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (   0)     3.2   
                                       no                                                                           24.3 ( 590)           
                                       yes                                                                          72.4 (1755)           
                                       <NA>                                                                          3.2 (  78)           
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (   0)     4.4   
                                       no                                                                           30.0 ( 726)           
                                       yes                                                                          65.6 (1590)           
                                       <NA>                                                                          4.4 ( 107)           
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (   0)     1.2   
                                       no                                                                           14.6 ( 354)           
                                       yes                                                                          84.2 (2041)           
                                       <NA>                                                                          1.2 (  28)           
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (   0)     1.4   
                                       no                                                                           23.4 ( 566)           
                                       yes                                                                          75.3 (1824)           
                                       <NA>                                                                          1.4 (  33)           
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (   0)     1.6   
                                       no                                                                           87.3 (2116)           
                                       yes                                                                          11.1 ( 269)           
                                       <NA>                                                                          1.6 (  38)           
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (   0)     1.5   
                                       no                                                                           12.2 ( 295)           
                                       yes                                                                          86.3 (2092)           
                                       <NA>                                                                          1.5 (  36)           
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (   0)     1.4   
                                       no                                                                           20.3 ( 491)           
                                       yes                                                                          78.3 (1898)           
                                       <NA>                                                                          1.4 (  34)           
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (   0)     6.9   
                                       No stroke diagnosed                                                          71.5 (1732)           
                                       Stroke diagnosed                                                             21.7 ( 525)           
                                       <NA>                                                                          6.9 ( 166)           
  sympt % (freq)                       missing                                                                       0.0 (   0)     0.0   
                                       Asymptomatic                                                                 11.1 ( 270)           
                                       TIA                                                                          39.7 ( 961)           
                                       minor stroke                                                                 16.8 ( 407)           
                                       Major stroke                                                                  9.9 ( 239)           
                                       Amaurosis fugax                                                              15.7 ( 380)           
                                       Four vessel disease                                                           1.6 (  38)           
                                       Vertebrobasilary TIA                                                          0.2 (   5)           
                                       Retinal infarction                                                            1.4 (  34)           
                                       Symptomatic, but aspecific symtoms                                            2.2 (  53)           
                                       Contralateral symptomatic occlusion                                           0.5 (  11)           
                                       retinal infarction                                                            0.2 (   6)           
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (   1)           
                                       retinal infarction + TIAs                                                     0.0 (   0)           
                                       Ocular ischemic syndrome                                                      0.7 (  16)           
                                       ischemisch glaucoom                                                           0.0 (   0)           
                                       subclavian steal syndrome                                                     0.1 (   2)           
                                       TGA                                                                           0.0 (   0)           
  Symptoms.5G % (freq)                 Asymptomatic                                                                 11.1 ( 270)     0.0   
                                       Ocular                                                                       16.3 ( 396)           
                                       Other                                                                         4.3 ( 105)           
                                       Retinal infarction                                                            1.7 (  40)           
                                       Stroke                                                                       26.7 ( 646)           
                                       TIA                                                                          39.9 ( 966)           
  AsymptSympt % (freq)                 Asymptomatic                                                                 11.1 ( 270)     0.0   
                                       Ocular and others                                                            22.3 ( 541)           
                                       Symptomatic                                                                  66.5 (1612)           
  AsymptSympt2G % (freq)               Asymptomatic                                                                 11.1 ( 270)     0.0   
                                       Symptomatic                                                                  88.9 (2153)           
  restenos % (freq)                    missing                                                                       0.0 (   0)     1.4   
                                       de novo                                                                      93.7 (2270)           
                                       restenosis                                                                    4.9 ( 118)           
                                       stenose bij angioseal na PTCA                                                 0.0 (   0)           
                                       <NA>                                                                          1.4 (  35)           
  stenose % (freq)                     missing                                                                       0.0 (   0)     2.0   
                                       0-49%                                                                         0.5 (  13)           
                                       50-70%                                                                        7.8 ( 190)           
                                       70-90%                                                                       46.5 (1127)           
                                       90-99%                                                                       38.3 ( 928)           
                                       100% (Occlusion)                                                              1.3 (  31)           
                                       NA                                                                            0.0 (   1)           
                                       50-99%                                                                        0.6 (  15)           
                                       70-99%                                                                        2.8 (  68)           
                                       99                                                                            0.1 (   2)           
                                       <NA>                                                                          2.0 (  48)           
  MedHx_CVD % (freq)                   No                                                                           36.9 ( 893)     0.0   
                                       yes                                                                          63.1 (1530)           
  CAD_history % (freq)                 Missing                                                                       0.0 (   0)     1.9   
                                       No history CAD                                                               66.9 (1620)           
                                       History CAD                                                                  31.2 ( 756)           
                                       <NA>                                                                          1.9 (  47)           
  PAOD % (freq)                        missing/no data                                                               0.0 (   0)     2.0   
                                       no                                                                           77.5 (1878)           
                                       yes                                                                          20.5 ( 497)           
                                       <NA>                                                                          2.0 (  48)           
  Peripheral.interv % (freq)           no                                                                           77.2 (1870)     2.9   
                                       yes                                                                          19.9 ( 482)           
                                       <NA>                                                                          2.9 (  71)           
  EP_composite % (freq)                No data available.                                                            0.0 (   0)     5.0   
                                       No composite endpoints                                                       70.6 (1711)           
                                       Composite endpoints                                                          24.3 ( 590)           
                                       <NA>                                                                          5.0 ( 122)           
  EP_composite_time (mean (SD))                                                                                    2.479 (1.109)    5.2   
  macmean0 (mean (SD))                                                                                             0.767 (1.183)   29.7   
  smcmean0 (mean (SD))                                                                                             1.985 (2.380)   29.9   
  Macrophages.bin % (freq)             no/minor                                                                     35.0 ( 847)    24.1   
                                       moderate/heavy                                                               40.9 ( 992)           
                                       <NA>                                                                         24.1 ( 584)           
  SMC.bin % (freq)                     no/minor                                                                     24.8 ( 602)    23.8   
                                       moderate/heavy                                                               51.3 (1244)           
                                       <NA>                                                                         23.8 ( 577)           
  neutrophils (mean (SD))                                                                                        147.151 (419.998) 87.5   
  Mast_cells_plaque (mean (SD))                                                                                  164.488 (163.771) 90.0   
  IPH.bin % (freq)                     no                                                                           30.8 ( 746)    23.5   
                                       yes                                                                          45.7 (1108)           
                                       <NA>                                                                         23.5 ( 569)           
  vessel_density_averaged (mean (SD))                                                                              8.317 (6.384)   35.1   
  Calc.bin % (freq)                    no/minor                                                                     41.6 (1007)    23.4   
                                       moderate/heavy                                                               35.1 ( 850)           
                                       <NA>                                                                         23.4 ( 566)           
  Collagen.bin % (freq)                no/minor                                                                     15.8 ( 382)    23.6   
                                       moderate/heavy                                                               60.6 (1469)           
                                       <NA>                                                                         23.6 ( 572)           
  Fat.bin_10 % (freq)                   <10%                                                                        22.4 ( 542)    23.3   
                                        >10%                                                                        54.3 (1316)           
                                       <NA>                                                                         23.3 ( 565)           
  Fat.bin_40 % (freq)                  <40%                                                                         56.2 (1362)    23.3   
                                       >40%                                                                         20.5 ( 496)           
                                       <NA>                                                                         23.3 ( 565)           
  OverallPlaquePhenotype % (freq)      atheromatous                                                                 19.8 ( 480)    23.7   
                                       fibroatheromatous                                                            27.8 ( 674)           
                                       fibrous                                                                      28.7 ( 695)           
                                       <NA>                                                                         23.7 ( 574)           
  IL6 (mean (SD))                                                                                                 98.812 (292.457) 78.2   
  IL6R_pg_ml_2015 (mean (SD))                                                                                    217.355 (248.551) 52.4   
  MCP1 (mean (SD))                                                                                               135.763 (120.028) 76.7   
  MCP1_pg_ml_2015 (mean (SD))                                                                                    600.444 (858.416) 50.5   

CEA patients with MCP1_pg_ml_2015

Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ml_2015.

AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
                                      Stratified by AsymptSympt2G
                                       level                                                                     Asymptomatic     
  n                                                                                                                  131          
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     50.4 ( 66)    
                                       UMC Utrecht                                                                  49.6 ( 65)    
  ORyear % (freq)                      No data available/missing                                                     0.0 (  0)    
                                       2002                                                                         10.7 ( 14)    
                                       2003                                                                          7.6 ( 10)    
                                       2004                                                                         17.6 ( 23)    
                                       2005                                                                          9.9 ( 13)    
                                       2006                                                                         10.7 ( 14)    
                                       2007                                                                         11.5 ( 15)    
                                       2008                                                                          7.6 ( 10)    
                                       2009                                                                          7.6 ( 10)    
                                       2010                                                                          5.3 (  7)    
                                       2011                                                                          6.1 (  8)    
                                       2012                                                                          5.3 (  7)    
                                       2013                                                                          0.0 (  0)    
                                       2014                                                                          0.0 (  0)    
                                       2015                                                                          0.0 (  0)    
                                       2016                                                                          0.0 (  0)    
                                       2017                                                                          0.0 (  0)    
                                       2018                                                                          0.0 (  0)    
                                       2019                                                                          0.0 (  0)    
  Age (mean (SD))                                                                                                 66.237 (9.184)  
  Gender % (freq)                      female                                                                       23.7 ( 31)    
                                       male                                                                         76.3 (100)    
  TC_finalCU (mean (SD))                                                                                         175.987 (47.184) 
  LDL_finalCU (mean (SD))                                                                                        102.781 (38.324) 
  HDL_finalCU (mean (SD))                                                                                         43.701 (14.754) 
  TG_finalCU (mean (SD))                                                                                         157.650 (89.246) 
  TC_final (mean (SD))                                                                                             4.558 (1.222)  
  LDL_final (mean (SD))                                                                                            2.662 (0.993)  
  HDL_final (mean (SD))                                                                                            1.132 (0.382)  
  TG_final (mean (SD))                                                                                             1.781 (1.008)  
  hsCRP_plasma (mean (SD))                                                                                         5.688 (19.440) 
  systolic (mean (SD))                                                                                           153.577 (24.327) 
  diastoli (mean (SD))                                                                                            80.622 (13.225) 
  GFR_MDRD (mean (SD))                                                                                            71.026 (20.424) 
  BMI (mean (SD))                                                                                                 26.623 (3.391)  
  KDOQI % (freq)                       No data available/missing                                                     0.0 (  0)    
                                       Normal kidney function                                                       17.6 ( 23)    
                                       CKD 2 (Mild)                                                                 49.6 ( 65)    
                                       CKD 3 (Moderate)                                                             28.2 ( 37)    
                                       CKD 4 (Severe)                                                                0.0 (  0)    
                                       CKD 5 (Failure)                                                               0.8 (  1)    
                                       <NA>                                                                          3.8 (  5)    
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (  0)    
                                       Underweight                                                                   0.8 (  1)    
                                       Normal                                                                       32.8 ( 43)    
                                       Overweight                                                                   51.1 ( 67)    
                                       Obese                                                                        13.0 ( 17)    
                                       <NA>                                                                          2.3 (  3)    
  SmokerStatus % (freq)                Current smoker                                                               30.5 ( 40)    
                                       Ex-smoker                                                                    57.3 ( 75)    
                                       Never smoked                                                                  9.9 ( 13)    
                                       <NA>                                                                          2.3 (  3)    
  AlcoholUse % (freq)                  No                                                                           38.2 ( 50)    
                                       Yes                                                                          59.5 ( 78)    
                                       <NA>                                                                          2.3 (  3)    
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 76.3 (100)    
                                       Diabetes                                                                     23.7 ( 31)    
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (  0)    
                                       no                                                                           23.7 ( 31)    
                                       yes                                                                          75.6 ( 99)    
                                       <NA>                                                                          0.8 (  1)    
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (  0)    
                                       no                                                                           30.5 ( 40)    
                                       yes                                                                          67.9 ( 89)    
                                       <NA>                                                                          1.5 (  2)    
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (  0)    
                                       no                                                                            9.9 ( 13)    
                                       yes                                                                          90.1 (118)    
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (  0)    
                                       no                                                                           14.5 ( 19)    
                                       yes                                                                          85.5 (112)    
                                       <NA>                                                                          0.0 (  0)    
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (  0)    
                                       no                                                                           89.3 (117)    
                                       yes                                                                          10.7 ( 14)    
                                       <NA>                                                                          0.0 (  0)    
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (  0)    
                                       no                                                                            6.1 (  8)    
                                       yes                                                                          93.1 (122)    
                                       <NA>                                                                          0.8 (  1)    
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (  0)    
                                       no                                                                           15.3 ( 20)    
                                       yes                                                                          84.7 (111)    
                                       <NA>                                                                          0.0 (  0)    
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (  0)    
                                       No stroke diagnosed                                                          80.2 (105)    
                                       Stroke diagnosed                                                             14.5 ( 19)    
                                       <NA>                                                                          5.3 (  7)    
  sympt % (freq)                       missing                                                                       0.0 (  0)    
                                       Asymptomatic                                                                100.0 (131)    
                                       TIA                                                                           0.0 (  0)    
                                       minor stroke                                                                  0.0 (  0)    
                                       Major stroke                                                                  0.0 (  0)    
                                       Amaurosis fugax                                                               0.0 (  0)    
                                       Four vessel disease                                                           0.0 (  0)    
                                       Vertebrobasilary TIA                                                          0.0 (  0)    
                                       Retinal infarction                                                            0.0 (  0)    
                                       Symptomatic, but aspecific symtoms                                            0.0 (  0)    
                                       Contralateral symptomatic occlusion                                           0.0 (  0)    
                                       retinal infarction                                                            0.0 (  0)    
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (  0)    
                                       retinal infarction + TIAs                                                     0.0 (  0)    
                                       Ocular ischemic syndrome                                                      0.0 (  0)    
                                       ischemisch glaucoom                                                           0.0 (  0)    
                                       subclavian steal syndrome                                                     0.0 (  0)    
                                       TGA                                                                           0.0 (  0)    
  Symptoms.5G % (freq)                 Asymptomatic                                                                100.0 (131)    
                                       Ocular                                                                        0.0 (  0)    
                                       Other                                                                         0.0 (  0)    
                                       Retinal infarction                                                            0.0 (  0)    
                                       Stroke                                                                        0.0 (  0)    
                                       TIA                                                                           0.0 (  0)    
  AsymptSympt % (freq)                 Asymptomatic                                                                100.0 (131)    
                                       Ocular and others                                                             0.0 (  0)    
                                       Symptomatic                                                                   0.0 (  0)    
  AsymptSympt2G % (freq)               Asymptomatic                                                                100.0 (131)    
                                       Symptomatic                                                                   0.0 (  0)    
  restenos % (freq)                    missing                                                                       0.0 (  0)    
                                       de novo                                                                      93.9 (123)    
                                       restenosis                                                                    2.3 (  3)    
                                       stenose bij angioseal na PTCA                                                 0.0 (  0)    
                                       <NA>                                                                          3.8 (  5)    
  stenose % (freq)                     missing                                                                       0.0 (  0)    
                                       0-49%                                                                         0.0 (  0)    
                                       50-70%                                                                        3.1 (  4)    
                                       70-90%                                                                       51.1 ( 67)    
                                       90-99%                                                                       41.2 ( 54)    
                                       100% (Occlusion)                                                              0.0 (  0)    
                                       NA                                                                            0.0 (  0)    
                                       50-99%                                                                        0.8 (  1)    
                                       70-99%                                                                        0.0 (  0)    
                                       99                                                                            0.0 (  0)    
                                       <NA>                                                                          3.8 (  5)    
  MedHx_CVD % (freq)                   No                                                                           38.9 ( 51)    
                                       yes                                                                          61.1 ( 80)    
  CAD_history % (freq)                 Missing                                                                       0.0 (  0)    
                                       No history CAD                                                               61.8 ( 81)    
                                       History CAD                                                                  38.2 ( 50)    
  PAOD % (freq)                        missing/no data                                                               0.0 (  0)    
                                       no                                                                           74.0 ( 97)    
                                       yes                                                                          26.0 ( 34)    
  Peripheral.interv % (freq)           no                                                                           74.0 ( 97)    
                                       yes                                                                          26.0 ( 34)    
                                       <NA>                                                                          0.0 (  0)    
  EP_composite % (freq)                No data available.                                                            0.0 (  0)    
                                       No composite endpoints                                                       67.2 ( 88)    
                                       Composite endpoints                                                          32.8 ( 43)    
                                       <NA>                                                                          0.0 (  0)    
  EP_composite_time (mean (SD))                                                                                    2.614 (0.931)  
  macmean0 (mean (SD))                                                                                             0.837 (1.088)  
  smcmean0 (mean (SD))                                                                                             2.152 (1.861)  
  Macrophages.bin % (freq)             no/minor                                                                     48.9 ( 64)    
                                       moderate/heavy                                                               50.4 ( 66)    
                                       <NA>                                                                          0.8 (  1)    
  SMC.bin % (freq)                     no/minor                                                                     22.9 ( 30)    
                                       moderate/heavy                                                               75.6 ( 99)    
                                       <NA>                                                                          1.5 (  2)    
  neutrophils (mean (SD))                                                                                        157.643 (507.380)
  Mast_cells_plaque (mean (SD))                                                                                  111.400 (112.037)
  IPH.bin % (freq)                     no                                                                           41.2 ( 54)    
                                       yes                                                                          58.0 ( 76)    
                                       <NA>                                                                          0.8 (  1)    
  vessel_density_averaged (mean (SD))                                                                              8.608 (6.547)  
                                      Stratified by AsymptSympt2G
                                       Symptomatic       p      test Missing
  n                                       1068                              
  Hospital % (freq)                       46.4 ( 496)     0.447       0.0   
                                          53.6 ( 572)                       
  ORyear % (freq)                          0.0 (   0)       NaN       0.0   
                                           3.9 (  42)                       
                                           9.4 ( 100)                       
                                          11.5 ( 123)                       
                                          11.1 ( 119)                       
                                          10.2 ( 109)                       
                                          10.5 ( 112)                       
                                           7.4 (  79)                       
                                           8.4 (  90)                       
                                           7.6 (  81)                       
                                           9.6 ( 102)                       
                                           8.3 (  89)                       
                                           2.0 (  21)                       
                                           0.1 (   1)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
  Age (mean (SD))                       68.940 (9.115)    0.001       0.0   
  Gender % (freq)                         31.4 ( 335)     0.088       0.0   
                                          68.6 ( 733)                       
  TC_finalCU (mean (SD))               183.526 (48.426)   0.174      33.5   
  LDL_finalCU (mean (SD))              109.377 (41.109)   0.183      39.7   
  HDL_finalCU (mean (SD))               45.809 (18.513)   0.318      36.4   
  TG_finalCU (mean (SD))               145.194 (84.818)   0.209      36.1   
  TC_final (mean (SD))                   4.753 (1.254)    0.174      33.5   
  LDL_final (mean (SD))                  2.833 (1.065)    0.183      39.7   
  HDL_final (mean (SD))                  1.186 (0.479)    0.318      36.4   
  TG_final (mean (SD))                   1.641 (0.958)    0.209      36.1   
  hsCRP_plasma (mean (SD))              16.551 (113.708)  0.380      38.8   
  systolic (mean (SD))                 155.790 (26.176)   0.397      14.0   
  diastoli (mean (SD))                  82.883 (13.573)   0.097      14.0   
  GFR_MDRD (mean (SD))                  71.866 (20.055)   0.658       3.6   
  BMI (mean (SD))                       26.323 (3.744)    0.386       4.2   
  KDOQI % (freq)                           0.0 (   0)       NaN       3.7   
                                          17.2 ( 184)                       
                                          53.2 ( 568)                       
                                          24.3 ( 260)                       
                                           1.2 (  13)                       
                                           0.4 (   4)                       
                                           3.7 (  39)                       
  BMI_WHO % (freq)                         0.0 (   0)       NaN       4.3   
                                           0.9 (  10)                       
                                          35.6 ( 380)                       
                                          46.2 ( 493)                       
                                          12.7 ( 136)                       
                                           4.6 (  49)                       
  SmokerStatus % (freq)                   36.2 ( 387)     0.077       3.8   
                                          45.6 ( 487)                       
                                          14.2 ( 152)                       
                                           3.9 (  42)                       
  AlcoholUse % (freq)                     33.3 ( 356)     0.347       4.1   
                                          62.4 ( 666)                       
                                           4.3 (  46)                       
  DiabetesStatus % (freq)                 77.3 ( 826)     0.882       0.0   
                                          22.7 ( 242)                       
  Hypertension.selfreport % (freq)         0.0 (   0)       NaN       2.0   
                                          26.7 ( 285)                       
                                          71.2 ( 760)                       
                                           2.2 (  23)                       
  Hypertension.selfreportdrug % (freq)     0.0 (   0)       NaN       2.7   
                                          33.0 ( 352)                       
                                          64.2 ( 686)                       
                                           2.8 (  30)                       
  Hypertension.composite % (freq)          0.0 (   0)       NaN       0.0   
                                          14.3 ( 153)                       
                                          85.7 ( 915)                       
  Hypertension.drugs % (freq)              0.0 (   0)       NaN       0.2   
                                          23.3 ( 249)                       
                                          76.5 ( 817)                       
                                           0.2 (   2)                       
  Med.anticoagulants % (freq)              0.0 (   0)       NaN       0.2   
                                          87.9 ( 939)                       
                                          11.9 ( 127)                       
                                           0.2 (   2)                       
  Med.all.antiplatelet % (freq)            0.0 (   0)       NaN       0.4   
                                          11.0 ( 118)                       
                                          88.6 ( 946)                       
                                           0.4 (   4)                       
  Med.Statin.LLD % (freq)                  0.0 (   0)       NaN       0.2   
                                          22.7 ( 242)                       
                                          77.2 ( 824)                       
                                           0.2 (   2)                       
  Stroke_Dx % (freq)                       0.0 (   0)       NaN       5.3   
                                          75.2 ( 803)                       
                                          19.5 ( 208)                       
                                           5.3 (  57)                       
  sympt % (freq)                           0.0 (   0)       NaN       0.0   
                                           0.0 (   0)                       
                                          46.3 ( 494)                       
                                          16.7 ( 178)                       
                                          12.3 ( 131)                       
                                          17.2 ( 184)                       
                                           2.2 (  23)                       
                                           0.2 (   2)                       
                                           1.4 (  15)                       
                                           2.7 (  29)                       
                                           0.7 (   7)                       
                                           0.3 (   3)                       
                                           0.1 (   1)                       
                                           0.0 (   0)                       
                                           0.1 (   1)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
  Symptoms.5G % (freq)                     0.0 (   0)    <0.001       0.0   
                                          17.3 ( 185)                       
                                           5.6 (  60)                       
                                           1.7 (  18)                       
                                          28.9 ( 309)                       
                                          46.4 ( 496)                       
  AsymptSympt % (freq)                     0.0 (   0)    <0.001       0.0   
                                          24.6 ( 263)                       
                                          75.4 ( 805)                       
  AsymptSympt2G % (freq)                   0.0 (   0)    <0.001       0.0   
                                         100.0 (1068)                       
  restenos % (freq)                        0.0 (   0)       NaN       2.3   
                                          94.8 (1012)                       
                                           3.2 (  34)                       
                                           0.0 (   0)                       
                                           2.1 (  22)                       
  stenose % (freq)                         0.0 (   0)       NaN       3.2   
                                           0.6 (   6)                       
                                           6.5 (  69)                       
                                          44.5 ( 475)                       
                                          42.7 ( 456)                       
                                           0.9 (  10)                       
                                           0.0 (   0)                       
                                           0.5 (   5)                       
                                           1.3 (  14)                       
                                           0.0 (   0)                       
                                           3.1 (  33)                       
  MedHx_CVD % (freq)                      36.9 ( 394)     0.719       0.0   
                                          63.1 ( 674)                       
  CAD_history % (freq)                     0.0 (   0)       NaN       0.0   
                                          69.9 ( 746)                       
                                          30.1 ( 322)                       
  PAOD % (freq)                            0.0 (   0)       NaN       0.0   
                                          79.6 ( 850)                       
                                          20.4 ( 218)                       
  Peripheral.interv % (freq)              82.5 ( 881)     0.043       0.3   
                                          17.2 ( 184)                       
                                           0.3 (   3)                       
  EP_composite % (freq)                    0.0 (   0)       NaN       0.8   
                                          74.3 ( 793)                       
                                          24.9 ( 266)                       
                                           0.8 (   9)                       
  EP_composite_time (mean (SD))          2.613 (1.094)    0.992       0.9   
  macmean0 (mean (SD))                   0.780 (1.229)    0.616       2.3   
  smcmean0 (mean (SD))                   1.904 (2.220)    0.223       2.7   
  Macrophages.bin % (freq)                47.5 ( 507)     0.586       1.9   
                                          50.5 ( 539)                       
                                           2.1 (  22)                       
  SMC.bin % (freq)                        32.1 ( 343)     0.088       1.8   
                                          66.0 ( 705)                       
                                           1.9 (  20)                       
  neutrophils (mean (SD))              172.872 (477.038)  0.876      82.0   
  Mast_cells_plaque (mean (SD))        183.284 (180.156)  0.056      86.2   
  IPH.bin % (freq)                        38.1 ( 407)     0.577       1.7   
                                          60.1 ( 642)                       
                                           1.8 (  19)                       
  vessel_density_averaged (mean (SD))    8.403 (6.461)    0.744       8.7   
 [ reached getOption("max.print") -- omitted 20 rows ]

CEA patients with MCP1_pg_ml_2015 and MCP1

Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ml_2015 and MCP1.


AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
                                      Stratified by AsymptSympt2G
                                       level                                                                     Asymptomatic     
  n                                                                                                                  161          
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     52.2 ( 84)    
                                       UMC Utrecht                                                                  47.8 ( 77)    
  ORyear % (freq)                      No data available/missing                                                     0.0 (  0)    
                                       2002                                                                         10.6 ( 17)    
                                       2003                                                                         11.8 ( 19)    
                                       2004                                                                         19.9 ( 32)    
                                       2005                                                                         13.7 ( 22)    
                                       2006                                                                          8.7 ( 14)    
                                       2007                                                                          9.3 ( 15)    
                                       2008                                                                          6.2 ( 10)    
                                       2009                                                                          6.2 ( 10)    
                                       2010                                                                          4.3 (  7)    
                                       2011                                                                          5.0 (  8)    
                                       2012                                                                          4.3 (  7)    
                                       2013                                                                          0.0 (  0)    
                                       2014                                                                          0.0 (  0)    
                                       2015                                                                          0.0 (  0)    
                                       2016                                                                          0.0 (  0)    
                                       2017                                                                          0.0 (  0)    
                                       2018                                                                          0.0 (  0)    
                                       2019                                                                          0.0 (  0)    
  Age (mean (SD))                                                                                                 65.901 (9.051)  
  Gender % (freq)                      female                                                                       23.0 ( 37)    
                                       male                                                                         77.0 (124)    
  TC_finalCU (mean (SD))                                                                                         179.199 (45.274) 
  LDL_finalCU (mean (SD))                                                                                        104.132 (37.590) 
  HDL_finalCU (mean (SD))                                                                                         44.749 (14.890) 
  TG_finalCU (mean (SD))                                                                                         158.699 (87.584) 
  TC_final (mean (SD))                                                                                             4.641 (1.173)  
  LDL_final (mean (SD))                                                                                            2.697 (0.974)  
  HDL_final (mean (SD))                                                                                            1.159 (0.386)  
  TG_final (mean (SD))                                                                                             1.793 (0.990)  
  hsCRP_plasma (mean (SD))                                                                                         6.846 (21.838) 
  systolic (mean (SD))                                                                                           152.838 (24.600) 
  diastoli (mean (SD))                                                                                            80.824 (12.855) 
  GFR_MDRD (mean (SD))                                                                                            70.440 (19.793) 
  BMI (mean (SD))                                                                                                 26.626 (3.572)  
  KDOQI % (freq)                       No data available/missing                                                     0.0 (  0)    
                                       Normal kidney function                                                       14.9 ( 24)    
                                       CKD 2 (Mild)                                                                 50.9 ( 82)    
                                       CKD 3 (Moderate)                                                             29.8 ( 48)    
                                       CKD 4 (Severe)                                                                0.0 (  0)    
                                       CKD 5 (Failure)                                                               0.6 (  1)    
                                       <NA>                                                                          3.7 (  6)    
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (  0)    
                                       Underweight                                                                   1.2 (  2)    
                                       Normal                                                                       32.3 ( 52)    
                                       Overweight                                                                   49.7 ( 80)    
                                       Obese                                                                        13.7 ( 22)    
                                       <NA>                                                                          3.1 (  5)    
  SmokerStatus % (freq)                Current smoker                                                               29.2 ( 47)    
                                       Ex-smoker                                                                    56.5 ( 91)    
                                       Never smoked                                                                 11.8 ( 19)    
                                       <NA>                                                                          2.5 (  4)    
  AlcoholUse % (freq)                  No                                                                           38.5 ( 62)    
                                       Yes                                                                          59.6 ( 96)    
                                       <NA>                                                                          1.9 (  3)    
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 78.3 (126)    
                                       Diabetes                                                                     21.7 ( 35)    
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (  0)    
                                       no                                                                           25.5 ( 41)    
                                       yes                                                                          73.9 (119)    
                                       <NA>                                                                          0.6 (  1)    
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (  0)    
                                       no                                                                           32.3 ( 52)    
                                       yes                                                                          66.5 (107)    
                                       <NA>                                                                          1.2 (  2)    
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (  0)    
                                       no                                                                           11.2 ( 18)    
                                       yes                                                                          88.8 (143)    
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (  0)    
                                       no                                                                           15.5 ( 25)    
                                       yes                                                                          83.9 (135)    
                                       <NA>                                                                          0.6 (  1)    
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (  0)    
                                       no                                                                           89.4 (144)    
                                       yes                                                                           9.9 ( 16)    
                                       <NA>                                                                          0.6 (  1)    
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (  0)    
                                       no                                                                            6.2 ( 10)    
                                       yes                                                                          92.5 (149)    
                                       <NA>                                                                          1.2 (  2)    
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (  0)    
                                       no                                                                           17.4 ( 28)    
                                       yes                                                                          82.0 (132)    
                                       <NA>                                                                          0.6 (  1)    
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (  0)    
                                       No stroke diagnosed                                                          80.1 (129)    
                                       Stroke diagnosed                                                             13.7 ( 22)    
                                       <NA>                                                                          6.2 ( 10)    
  sympt % (freq)                       missing                                                                       0.0 (  0)    
                                       Asymptomatic                                                                100.0 (161)    
                                       TIA                                                                           0.0 (  0)    
                                       minor stroke                                                                  0.0 (  0)    
                                       Major stroke                                                                  0.0 (  0)    
                                       Amaurosis fugax                                                               0.0 (  0)    
                                       Four vessel disease                                                           0.0 (  0)    
                                       Vertebrobasilary TIA                                                          0.0 (  0)    
                                       Retinal infarction                                                            0.0 (  0)    
                                       Symptomatic, but aspecific symtoms                                            0.0 (  0)    
                                       Contralateral symptomatic occlusion                                           0.0 (  0)    
                                       retinal infarction                                                            0.0 (  0)    
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (  0)    
                                       retinal infarction + TIAs                                                     0.0 (  0)    
                                       Ocular ischemic syndrome                                                      0.0 (  0)    
                                       ischemisch glaucoom                                                           0.0 (  0)    
                                       subclavian steal syndrome                                                     0.0 (  0)    
                                       TGA                                                                           0.0 (  0)    
  Symptoms.5G % (freq)                 Asymptomatic                                                                100.0 (161)    
                                       Ocular                                                                        0.0 (  0)    
                                       Other                                                                         0.0 (  0)    
                                       Retinal infarction                                                            0.0 (  0)    
                                       Stroke                                                                        0.0 (  0)    
                                       TIA                                                                           0.0 (  0)    
  AsymptSympt % (freq)                 Asymptomatic                                                                100.0 (161)    
                                       Ocular and others                                                             0.0 (  0)    
                                       Symptomatic                                                                   0.0 (  0)    
  AsymptSympt2G % (freq)               Asymptomatic                                                                100.0 (161)    
                                       Symptomatic                                                                   0.0 (  0)    
  restenos % (freq)                    missing                                                                       0.0 (  0)    
                                       de novo                                                                      93.2 (150)    
                                       restenosis                                                                    3.7 (  6)    
                                       stenose bij angioseal na PTCA                                                 0.0 (  0)    
                                       <NA>                                                                          3.1 (  5)    
  stenose % (freq)                     missing                                                                       0.0 (  0)    
                                       0-49%                                                                         0.0 (  0)    
                                       50-70%                                                                        2.5 (  4)    
                                       70-90%                                                                       50.9 ( 82)    
                                       90-99%                                                                       42.9 ( 69)    
                                       100% (Occlusion)                                                              0.0 (  0)    
                                       NA                                                                            0.0 (  0)    
                                       50-99%                                                                        0.6 (  1)    
                                       70-99%                                                                        0.0 (  0)    
                                       99                                                                            0.0 (  0)    
                                       <NA>                                                                          3.1 (  5)    
  MedHx_CVD % (freq)                   No                                                                           37.3 ( 60)    
                                       yes                                                                          62.7 (101)    
  CAD_history % (freq)                 Missing                                                                       0.0 (  0)    
                                       No history CAD                                                               59.0 ( 95)    
                                       History CAD                                                                  41.0 ( 66)    
  PAOD % (freq)                        missing/no data                                                               0.0 (  0)    
                                       no                                                                           73.9 (119)    
                                       yes                                                                          26.1 ( 42)    
  Peripheral.interv % (freq)           no                                                                           72.7 (117)    
                                       yes                                                                          27.3 ( 44)    
                                       <NA>                                                                          0.0 (  0)    
  EP_composite % (freq)                No data available.                                                            0.0 (  0)    
                                       No composite endpoints                                                       68.3 (110)    
                                       Composite endpoints                                                          31.7 ( 51)    
                                       <NA>                                                                          0.0 (  0)    
  EP_composite_time (mean (SD))                                                                                    2.579 (0.961)  
  macmean0 (mean (SD))                                                                                             0.802 (1.072)  
  smcmean0 (mean (SD))                                                                                             2.445 (2.594)  
  Macrophages.bin % (freq)             no/minor                                                                     50.3 ( 81)    
                                       moderate/heavy                                                               49.1 ( 79)    
                                       <NA>                                                                          0.6 (  1)    
  SMC.bin % (freq)                     no/minor                                                                     21.7 ( 35)    
                                       moderate/heavy                                                               77.0 (124)    
                                       <NA>                                                                          1.2 (  2)    
  neutrophils (mean (SD))                                                                                        133.447 (437.032)
  Mast_cells_plaque (mean (SD))                                                                                  123.389 (135.924)
  IPH.bin % (freq)                     no                                                                           39.1 ( 63)    
                                       yes                                                                          60.2 ( 97)    
                                       <NA>                                                                          0.6 (  1)    
  vessel_density_averaged (mean (SD))                                                                              8.837 (6.727)  
                                      Stratified by AsymptSympt2G
                                       Symptomatic       p      test Missing
  n                                       1168                              
  Hospital % (freq)                       46.8 ( 547)     0.235       0.0   
                                          53.2 ( 621)                       
  ORyear % (freq)                          0.0 (   0)       NaN       0.0   
                                           4.8 (  56)                       
                                          10.6 ( 124)                       
                                          12.2 ( 142)                       
                                          13.3 ( 155)                       
                                           9.9 ( 116)                       
                                           9.6 ( 112)                       
                                           6.8 (  79)                       
                                           7.7 (  90)                       
                                           6.9 (  81)                       
                                           8.7 ( 102)                       
                                           7.6 (  89)                       
                                           1.8 (  21)                       
                                           0.1 (   1)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
  Age (mean (SD))                       68.788 (9.077)   <0.001       0.0   
  Gender % (freq)                         30.4 ( 355)     0.066       0.0   
                                          69.6 ( 813)                       
  TC_finalCU (mean (SD))               184.078 (48.333)   0.322      32.8   
  LDL_finalCU (mean (SD))              109.761 (41.318)   0.206      39.9   
  HDL_finalCU (mean (SD))               45.803 (18.219)   0.570      36.2   
  TG_finalCU (mean (SD))               145.901 (83.176)   0.141      35.7   
  TC_final (mean (SD))                   4.768 (1.252)    0.322      32.8   
  LDL_final (mean (SD))                  2.843 (1.070)    0.206      39.9   
  HDL_final (mean (SD))                  1.186 (0.472)    0.570      36.2   
  TG_final (mean (SD))                   1.649 (0.940)    0.141      35.7   
  hsCRP_plasma (mean (SD))              16.179 (110.739)  0.394      40.6   
  systolic (mean (SD))                 155.713 (26.406)   0.230      13.5   
  diastoli (mean (SD))                  82.863 (13.542)   0.097      13.5   
  GFR_MDRD (mean (SD))                  71.890 (20.127)   0.400       3.5   
  BMI (mean (SD))                       26.352 (3.765)    0.392       4.4   
  KDOQI % (freq)                           0.0 (   0)       NaN       3.6   
                                          17.4 ( 203)                       
                                          53.3 ( 623)                       
                                          24.0 ( 280)                       
                                           1.3 (  15)                       
                                           0.4 (   5)                       
                                           3.6 (  42)                       
  BMI_WHO % (freq)                         0.0 (   0)       NaN       4.6   
                                           0.9 (  11)                       
                                          35.5 ( 415)                       
                                          45.6 ( 533)                       
                                          13.1 ( 153)                       
                                           4.8 (  56)                       
  SmokerStatus % (freq)                   36.0 ( 421)     0.070       4.0   
                                          45.6 ( 533)                       
                                          14.1 ( 165)                       
                                           4.2 (  49)                       
  AlcoholUse % (freq)                     33.6 ( 393)     0.213       3.9   
                                          62.2 ( 726)                       
                                           4.2 (  49)                       
  DiabetesStatus % (freq)                 77.2 ( 902)     0.846       0.0   
                                          22.8 ( 266)                       
  Hypertension.selfreport % (freq)         0.0 (   0)       NaN       1.9   
                                          26.6 ( 311)                       
                                          71.3 ( 833)                       
                                           2.1 (  24)                       
  Hypertension.selfreportdrug % (freq)     0.0 (   0)       NaN       2.4   
                                          33.0 ( 385)                       
                                          64.5 ( 753)                       
                                           2.6 (  30)                       
  Hypertension.composite % (freq)          0.0 (   0)       NaN       0.0   
                                          14.1 ( 165)                       
                                          85.9 (1003)                       
  Hypertension.drugs % (freq)              0.0 (   0)       NaN       0.2   
                                          22.8 ( 266)                       
                                          77.1 ( 900)                       
                                           0.2 (   2)                       
  Med.anticoagulants % (freq)              0.0 (   0)       NaN       0.2   
                                          87.9 (1027)                       
                                          11.9 ( 139)                       
                                           0.2 (   2)                       
  Med.all.antiplatelet % (freq)            0.0 (   0)       NaN       0.5   
                                          10.9 ( 127)                       
                                          88.8 (1037)                       
                                           0.3 (   4)                       
  Med.Statin.LLD % (freq)                  0.0 (   0)       NaN       0.2   
                                          23.1 ( 270)                       
                                          76.7 ( 896)                       
                                           0.2 (   2)                       
  Stroke_Dx % (freq)                       0.0 (   0)       NaN       5.5   
                                          75.5 ( 882)                       
                                          19.1 ( 223)                       
                                           5.4 (  63)                       
  sympt % (freq)                           0.0 (   0)       NaN       0.0   
                                           0.0 (   0)                       
                                          46.5 ( 543)                       
                                          17.1 ( 200)                       
                                          11.6 ( 136)                       
                                          17.0 ( 198)                       
                                           2.1 (  25)                       
                                           0.2 (   2)                       
                                           1.4 (  16)                       
                                           3.1 (  36)                       
                                           0.6 (   7)                       
                                           0.3 (   3)                       
                                           0.1 (   1)                       
                                           0.0 (   0)                       
                                           0.1 (   1)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
                                           0.0 (   0)                       
  Symptoms.5G % (freq)                     0.0 (   0)    <0.001       0.0   
                                          17.0 ( 199)                       
                                           5.9 (  69)                       
                                           1.6 (  19)                       
                                          28.8 ( 336)                       
                                          46.7 ( 545)                       
  AsymptSympt % (freq)                     0.0 (   0)    <0.001       0.0   
                                          24.6 ( 287)                       
                                          75.4 ( 881)                       
  AsymptSympt2G % (freq)                   0.0 (   0)    <0.001       0.0   
                                         100.0 (1168)                       
  restenos % (freq)                        0.0 (   0)       NaN       2.0   
                                          95.0 (1110)                       
                                           3.1 (  36)                       
                                           0.0 (   0)                       
                                           1.9 (  22)                       
  stenose % (freq)                         0.0 (   0)       NaN       2.9   
                                           0.6 (   7)                       
                                           6.2 (  73)                       
                                          44.5 ( 520)                       
                                          43.2 ( 505)                       
                                           0.9 (  11)                       
                                           0.0 (   0)                       
                                           0.4 (   5)                       
                                           1.2 (  14)                       
                                           0.0 (   0)                       
                                           2.8 (  33)                       
  MedHx_CVD % (freq)                      36.7 ( 429)     0.964       0.0   
                                          63.3 ( 739)                       
  CAD_history % (freq)                     0.0 (   0)       NaN       0.0   
                                          69.1 ( 807)                       
                                          30.9 ( 361)                       
  PAOD % (freq)                            0.0 (   0)       NaN       0.0   
                                          79.9 ( 933)                       
                                          20.1 ( 235)                       
  Peripheral.interv % (freq)              83.0 ( 969)     0.004       0.2   
                                          16.8 ( 196)                       
                                           0.3 (   3)                       
  EP_composite % (freq)                    0.0 (   0)       NaN       0.8   
                                          73.8 ( 862)                       
                                          25.3 ( 295)                       
                                           0.9 (  11)                       
  EP_composite_time (mean (SD))          2.611 (1.129)    0.735       1.0   
  macmean0 (mean (SD))                   0.821 (1.274)    0.864       2.2   
  smcmean0 (mean (SD))                   1.924 (2.232)    0.007       2.5   
  Macrophages.bin % (freq)                45.8 ( 535)     0.314       1.8   
                                          52.2 ( 610)                       
                                           2.0 (  23)                       
  SMC.bin % (freq)                        32.4 ( 379)     0.018       1.7   
                                          65.8 ( 769)                       
                                           1.7 (  20)                       
  neutrophils (mean (SD))              158.140 (448.512)  0.754      81.0   
  Mast_cells_plaque (mean (SD))        173.244 (168.601)  0.097      83.7   
  IPH.bin % (freq)                        36.5 ( 426)     0.526       1.5   
                                          61.9 ( 723)                       
                                           1.6 (  19)                       
  vessel_density_averaged (mean (SD))    8.434 (6.386)    0.474       8.0   
 [ reached getOption("max.print") -- omitted 20 rows ]

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, and inverse-rank normal transformation (standardise). We know that the proteins are not normally distributed and therefore we will standardise them as follows:

z = ( x - μ ) / σ

Where for each sample, x equals the value of the variable, μ (mu) equals the mean of x, and σ (sigma) equals the standard deviation of x.

MCP1 plaque levels: experiment 2

We will explore the plaque levels. As noted above, we will use MCP1_pg_ml_2015, this was experiment 2 in 2015 on the LUMINEX-platform and measurements are in pg/mL.


summary(AEDB.CEA$MCP1_pg_ml_2015)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
    0.66   101.34   298.76   600.44   770.98 10181.08     1224 
do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary))
             Min.  1st Qu.  Median     Mean  3rd Qu.     Max. NA's
Asymptomatic 9.36  71.1650 152.220 405.1822 537.9100  2669.59  139
Symptomatic  0.66 114.9425 314.625 624.3948 792.4225 10181.08 1085
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
p1 

p3

# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p3)

MCP1 plaque levels: experiment 1

We will explore the plaque levels. As noted above, we will use MCP1, this was experiment 1 on the LUMINEX-platform and measurements are in pg/mL.


# summary(AEDB.CEA$MCP1)
# 
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
# 
attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
detach(AEDB.CEA)

summary(AEDB.CEA$MCP1)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  3.865  58.057 103.811 137.960 180.297 926.273    1867 
do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
                  Min.  1st Qu.    Median     Mean  3rd Qu.     Max. NA's
Asymptomatic 15.578813 45.31926  77.84731 119.4878 126.1851 846.5306  184
Symptomatic   3.864774 60.54905 111.87004 141.3406 186.4375 926.2729 1683
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.
p1 

p3

# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p3)

Correlations between MCP1 plaque levels and transformations

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2.

p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 1",
                        ylab = "experiment 2",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

Preliminary conclusion data exploration

Based on the inverse-rank normal transformation we conclude there are no outliers and the data approximates a normal distribution. We will apply inverse-rank normal transformation on all proteins and focus the analysis on MCP1 in plaque.

Analyses

The analyses are focused on three elements:

  1. plaque vulnerability phenotypes
  2. clinical status at inclusion (symptoms)
  3. secondary clinical outcome during three (3) years of follow-up

Covariates & other variables

  1. Age (continuous in 1-year increment). [Age]
  2. Sex (male vs. female). [Gender]
  3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
  4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [DiabetesStatus]
  5. Smoking (current, ex-, never). [SmokerStatus]
  6. LDL-C levels (continuous). [LDL_final]
  7. Use of lipid-lowering drugs. [Med.Statin.LLD]
  8. Use of antiplatelet drugs. [Med.all.antiplatelet]
  9. eGFR (continuous). [GFR_MDRD]
  10. BMI (continuous). [BMI]
  11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combination of [CAD_history, Stroke_history, Peripheral.interv]
  12. Level of stenosis (50-70% vs. 70-99%). [stenose]
  13. Year of surgery [ORdate_year] as we discovered in Van Lammeren et al. the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

Models

We will analyze the data through four different models

  • Model 1: adjusted for age, sex, and year of surgery
  • Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:

  • Percentage of macrophages (continuous trait)
  • Percentage of SMCs (continuous trait)
  • Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
  • Presence of moderate/heavy calcifications (binary trait)
  • Presence of moderate/heavy collagen content (binary trait)
  • Presence of lipid core no/<10% vs. >10% (binary trait)
  • Presence of intraplaque hemorrhage (binary trait)

Quantitative traits

We inspect the plaque characteristics, and inverse-rank normal transformation continuous phenotypes.


# macrophages
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7671  0.9967 15.1000     720 
min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

Minimum value % macrophages: 0.
AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# smooth muscle cells
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7671  0.9967 15.1000     720 
min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

Minimum value % smooth muscle cells: 0.
AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# vessel density
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$vessel_density_averaged)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   4.000   7.000   8.318  11.300  48.000     850 
min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
[1] 0
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.
AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed (‘unstable’) as compared to ‘stable’.


AEDB.CEA$MAC_SMC_ratio <- AEDB.CEA$macmean0 / AEDB.CEA$smcmean0

AEDB.CEA$MAC_SMC_ratio_rank  <- qnorm((rank(AEDB.CEA$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MAC_SMC_ratio)))


cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Macrophages_rank)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.3161 -0.6703  0.0000  0.0020  0.6745  3.4375     720 
summary(AEDB.CEA$SMC_rank)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.6939 -0.6736  0.0015  0.0006  0.6740  3.4368     724 
summary(AEDB.CEA$MAC_SMC_ratio_rank)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.3364 -0.6740  0.0000  0.0013  0.6740  2.7533     728 
ggpubr::gghistogram(AEDB.CEA, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

Binary traits


# calcification
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Calc.bin)
      no/minor moderate/heavy           NA's 
          1007            850            566 
contrasts(AEDB.CEA$Calc.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())

rm(df)

# collagen
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Collagen.bin)
      no/minor moderate/heavy           NA's 
           382           1469            572 
contrasts(AEDB.CEA$Collagen.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())

rm(df)

# fat 10%
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Fat.bin_10)
 <10%  >10%  NA's 
  542  1316   565 
contrasts(AEDB.CEA$Fat.bin_10)
       >10%
 <10%     0
 >10%     1
AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())

rm(df)

# macrophages binned
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Macrophages.bin)
      no/minor moderate/heavy           NA's 
           847            992            584 
contrasts(AEDB.CEA$Macrophages.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_binned)) %>%
  group_by(Gender, MAC_binned) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())

rm(df)

# macrophages grouped
cat("Summary of data.\n")
Summary of data.
AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)
-888    0    1    2    3 NA's 
   6  173  674  786  206  578 
contrasts(AEDB.CEA$macrophages)
     0 1 2 3
-888 0 0 0 0
0    1 0 0 0
1    0 1 0 0
2    0 0 1 0
3    0 0 0 1
AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_grouped)) %>%
  group_by(Gender, MAC_grouped) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (grouped)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())

rm(df)

# SMC binned
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$SMC.bin)
      no/minor moderate/heavy           NA's 
           602           1244            577 
contrasts(AEDB.CEA$SMC.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_binned)) %>%
  group_by(Gender, SMC_binned) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())

rm(df)

# SMC grouped
cat("Summary of data.\n")
Summary of data.
AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)
-888    0    1    2    3 NA's 
   4   44  558  908  336  573 
contrasts(AEDB.CEA$smc)
     0 1 2 3
-888 0 0 0 0
0    1 0 0 0
1    0 1 0 0
2    0 0 1 0
3    0 0 0 1
AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_grouped)) %>%
  group_by(Gender, SMC_grouped) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (grouped)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())

rm(df)


# IPH
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$IPH.bin)
  no  yes NA's 
 746 1108  569 
contrasts(AEDB.CEA$IPH.bin)
    yes
no    0
yes   1
AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())

rm(df)

# Symptoms
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$AsymptSympt)
     Asymptomatic Ocular and others       Symptomatic 
              270               541              1612 
contrasts(AEDB.CEA$AsymptSympt)
                  Ocular and others Symptomatic
Asymptomatic                      0           0
Ocular and others                 1           0
Symptomatic                       0           1
AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 
`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())

rm(df)

Correlations between MCP1 plaque levels and surgery year

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).

p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 


p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 2, [pg/mL]",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p2 


rm(p1, p2)

In this section we make some variables to assist with analysis.

AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN.RANK = c("MCP1_pg_ml_2015_rank", "MCP1_rank")

TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "MAC_SMC_ratio_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")


# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.   BMI (continuous). [BMI]
# 11.   History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.   Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as.numeric(year(AEDB.CEA$dateok))

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`
summary(AEDB.CEA$ORdate_epoch)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  11770   13132   14518   14567   15860   18250 
cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")

Summary of 'year of surgery' in 'years' (); coded as `factor()`
table(AEDB.CEA$ORdate_year)

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 
  81  157  190  185  183  152  138  182  159  164  176  149  163   76   85   65   66   52 
COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ml_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale         ORdate_year  
        -254.43741             0.06422             0.32420             0.12666  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.98066 -0.58281 -0.01477  0.58485  3.01698 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -2.535e+02  1.846e+01 -13.732  < 2e-16 ***
currentDF[, TRAIT]  6.552e-02  2.761e-02   2.373   0.0178 *  
Age                 1.944e-03  2.918e-03   0.666   0.5055    
Gendermale          3.236e-01  5.775e-02   5.603 2.63e-08 ***
ORdate_year         1.261e-01  9.207e-03  13.698  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9087 on 1166 degrees of freedom
Multiple R-squared:  0.1648,    Adjusted R-squared:  0.162 
F-statistic: 57.54 on 4 and 1166 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.065515 
Standard error............: 0.027613 
Odds ratio (effect size)..: 1.068 
Lower 95% CI..............: 1.011 
Upper 95% CI..............: 1.127 
T-value...................: 2.372657 
P-value...................: 0.01782216 
R^2.......................: 0.164841 
Adjusted r^2..............: 0.161976 
Sample size of AE DB......: 2423 
Sample size of model......: 1171 
Missing data %............: 51.67148 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale         ORdate_year  
         -232.0776             -0.0943              0.3013              0.1155  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.15076 -0.58587 -0.02393  0.55488  3.09880 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -2.322e+02  1.827e+01 -12.713  < 2e-16 ***
currentDF[, TRAIT] -9.502e-02  2.851e-02  -3.333 0.000887 ***
Age                -4.616e-04  2.944e-03  -0.157 0.875435    
Gendermale          3.012e-01  5.795e-02   5.198 2.38e-07 ***
ORdate_year         1.156e-01  9.109e-03  12.694  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.904 on 1162 degrees of freedom
Multiple R-squared:  0.1708,    Adjusted R-squared:  0.168 
F-statistic: 59.84 on 4 and 1162 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.095019 
Standard error............: 0.02851 
Odds ratio (effect size)..: 0.909 
Lower 95% CI..............: 0.86 
Upper 95% CI..............: 0.962 
T-value...................: -3.332864 
P-value...................: 0.0008866462 
R^2.......................: 0.170817 
Adjusted r^2..............: 0.167963 
Sample size of AE DB......: 2423 
Sample size of model......: 1167 
Missing data %............: 51.83657 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale         ORdate_year  
         -252.3972              0.1248              0.2862              0.1257  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.95999 -0.57823 -0.00289  0.55678  3.03063 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -2.520e+02  1.785e+01 -14.117  < 2e-16 ***
currentDF[, TRAIT]  1.246e-01  2.747e-02   4.537 6.31e-06 ***
Age                 7.136e-04  2.899e-03   0.246    0.806    
Gendermale          2.861e-01  5.803e-02   4.931 9.38e-07 ***
ORdate_year         1.254e-01  8.904e-03  14.085  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9011 on 1160 degrees of freedom
Multiple R-squared:  0.1775,    Adjusted R-squared:  0.1747 
F-statistic: 62.59 on 4 and 1160 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.124615 
Standard error............: 0.027468 
Odds ratio (effect size)..: 1.133 
Lower 95% CI..............: 1.073 
Upper 95% CI..............: 1.195 
T-value...................: 4.536768 
P-value...................: 6.305959e-06 
R^2.......................: 0.17752 
Adjusted r^2..............: 0.174684 
Sample size of AE DB......: 2423 
Sample size of model......: 1165 
Missing data %............: 51.91911 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale         ORdate_year  
        -228.08372            -0.06221             0.33587             0.11352  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.04767 -0.60662  0.00131  0.57990  3.05690 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -2.268e+02  1.916e+01 -11.838  < 2e-16 ***
currentDF[, TRAIT] -6.247e-02  2.862e-02  -2.183   0.0293 *  
Age                 1.921e-03  3.059e-03   0.628   0.5302    
Gendermale          3.356e-01  6.036e-02   5.561 3.38e-08 ***
ORdate_year         1.128e-01  9.561e-03  11.801  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9201 on 1090 degrees of freedom
Multiple R-squared:  0.1558,    Adjusted R-squared:  0.1527 
F-statistic: 50.28 on 4 and 1090 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.062472 
Standard error............: 0.02862 
Odds ratio (effect size)..: 0.939 
Lower 95% CI..............: 0.888 
Upper 95% CI..............: 0.994 
T-value...................: -2.182858 
P-value...................: 0.02925907 
R^2.......................: 0.155768 
Adjusted r^2..............: 0.15267 
Sample size of AE DB......: 2423 
Sample size of model......: 1095 
Missing data %............: 54.80809 

Analysis of MCP1_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale         ORdate_year  
          422.8331              0.1222              0.2600             -0.2111  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4317 -0.6291 -0.0261  0.6543  2.8355 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        414.987584  75.061156   5.529 4.99e-08 ***
currentDF[, TRAIT]   0.121339   0.038035   3.190   0.0015 ** 
Age                 -0.006268   0.004724  -1.327   0.1851    
Gendermale           0.263235   0.090556   2.907   0.0038 ** 
ORdate_year         -0.206979   0.037471  -5.524 5.13e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9598 on 550 degrees of freedom
Multiple R-squared:  0.0847,    Adjusted R-squared:  0.07804 
F-statistic: 12.72 on 4 and 550 DF,  p-value: 6.54e-10

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.121339 
Standard error............: 0.038035 
Odds ratio (effect size)..: 1.129 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.216 
T-value...................: 3.19016 
P-value...................: 0.001502979 
R^2.......................: 0.084699 
Adjusted r^2..............: 0.078042 
Sample size of AE DB......: 2423 
Sample size of model......: 555 
Missing data %............: 77.09451 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale         ORdate_year  
         485.13156            -0.22645            -0.01251             0.22171            -0.24174  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2040 -0.6017 -0.0439  0.6538  2.7241 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        485.131555  74.828140   6.483 2.01e-10 ***
currentDF[, TRAIT]  -0.226449   0.039572  -5.722 1.73e-08 ***
Age                 -0.012514   0.004716  -2.654   0.0082 ** 
Gendermale           0.221712   0.089045   2.490   0.0131 *  
ORdate_year         -0.241741   0.037348  -6.473 2.14e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9371 on 547 degrees of freedom
Multiple R-squared:  0.1219,    Adjusted R-squared:  0.1155 
F-statistic: 18.98 on 4 and 547 DF,  p-value: 1.253e-14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.226449 
Standard error............: 0.039572 
Odds ratio (effect size)..: 0.797 
Lower 95% CI..............: 0.738 
Upper 95% CI..............: 0.862 
T-value...................: -5.722399 
P-value...................: 1.731767e-08 
R^2.......................: 0.121879 
Adjusted r^2..............: 0.115458 
Sample size of AE DB......: 2423 
Sample size of model......: 552 
Missing data %............: 77.21832 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale         ORdate_year  
         469.90060             0.22103            -0.01003             0.21847            -0.23424  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3533 -0.5974 -0.0586  0.6573  2.9781 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        469.90060   73.95935   6.353 4.44e-10 ***
currentDF[, TRAIT]   0.22102    0.03612   6.119 1.80e-09 ***
Age                 -0.01003    0.00464  -2.162   0.0311 *  
Gendermale           0.21847    0.08873   2.462   0.0141 *  
ORdate_year         -0.23424    0.03692  -6.345 4.68e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.933 on 546 degrees of freedom
Multiple R-squared:  0.1294,    Adjusted R-squared:  0.123 
F-statistic: 20.29 on 4 and 546 DF,  p-value: 1.346e-15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.221025 
Standard error............: 0.036122 
Odds ratio (effect size)..: 1.247 
Lower 95% CI..............: 1.162 
Upper 95% CI..............: 1.339 
T-value...................: 6.118872 
P-value...................: 1.799303e-09 
R^2.......................: 0.129413 
Adjusted r^2..............: 0.123035 
Sample size of AE DB......: 2423 
Sample size of model......: 551 
Missing data %............: 77.2596 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)

Coefficients:
(Intercept)   Gendermale  ORdate_year  
   427.9795       0.2941      -0.2137  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4045 -0.5978 -0.0351  0.6466  2.6590 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        407.491047  77.437980   5.262 2.06e-07 ***
currentDF[, TRAIT]  -0.055611   0.050753  -1.096  0.27369    
Age                 -0.006762   0.004796  -1.410  0.15917    
Gendermale           0.296448   0.092030   3.221  0.00135 ** 
ORdate_year         -0.203215   0.038660  -5.257 2.12e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9662 on 539 degrees of freedom
Multiple R-squared:  0.07477,   Adjusted R-squared:  0.0679 
F-statistic: 10.89 on 4 and 539 DF,  p-value: 1.697e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.055611 
Standard error............: 0.050753 
Odds ratio (effect size)..: 0.946 
Lower 95% CI..............: 0.856 
Upper 95% CI..............: 1.045 
T-value...................: -1.09571 
P-value...................: 0.2736949 
R^2.......................: 0.07477 
Adjusted r^2..............: 0.067904 
Sample size of AE DB......: 2423 
Sample size of model......: 544 
Missing data %............: 77.54849 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ml_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + ORdate_year, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age           ORdate_year  
           310.77553              -0.34903               0.02309              -0.15567  

Degrees of Freedom: 1180 Total (i.e. Null);  1177 Residual
Null Deviance:      1637 
Residual Deviance: 1513     AIC: 1521

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8216  -1.0490  -0.6315   1.0837   2.0978  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          312.506897  44.478670   7.026 2.13e-12 ***
currentDF[, PROTEIN]  -0.340451   0.068997  -4.934 8.04e-07 ***
Age                    0.023128   0.006798   3.402 0.000669 ***
Gendermale            -0.109930   0.134724  -0.816 0.414521    
ORdate_year           -0.156493   0.022190  -7.052 1.76e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1636.6  on 1180  degrees of freedom
Residual deviance: 1512.0  on 1176  degrees of freedom
AIC: 1522

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.340451 
Standard error............: 0.068997 
Odds ratio (effect size)..: 0.711 
Lower 95% CI..............: 0.621 
Upper 95% CI..............: 0.814 
Z-value...................: -4.934311 
P-value...................: 8.04341e-07 
Hosmer and Lemeshow r^2...: 0.07616 
Cox and Snell r^2.........: 0.100162 
Nagelkerke's pseudo r^2...: 0.133573 
Sample size of AE DB......: 2423 
Sample size of model......: 1181 
Missing data %............: 51.25877 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              1.3403               -0.2873  

Degrees of Freedom: 1181 Total (i.e. Null);  1180 Residual
Null Deviance:      1217 
Residual Deviance: 1202     AIC: 1206

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2054   0.5389   0.6456   0.7150   1.0194  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -17.700869  50.816422  -0.348 0.727593    
currentDF[, PROTEIN]  -0.304533   0.079925  -3.810 0.000139 ***
Age                    0.004359   0.007869   0.554 0.579607    
Gendermale             0.066303   0.158551   0.418 0.675814    
ORdate_year            0.009316   0.025343   0.368 0.713171    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1217.1  on 1181  degrees of freedom
Residual deviance: 1200.8  on 1177  degrees of freedom
AIC: 1210.8

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.304533 
Standard error............: 0.079925 
Odds ratio (effect size)..: 0.737 
Lower 95% CI..............: 0.631 
Upper 95% CI..............: 0.863 
Z-value...................: -3.810259 
P-value...................: 0.0001388211 
Hosmer and Lemeshow r^2...: 0.013321 
Cox and Snell r^2.........: 0.013622 
Nagelkerke's pseudo r^2...: 0.021189 
Sample size of AE DB......: 2423 
Sample size of model......: 1182 
Missing data %............: 51.2175 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale           ORdate_year  
           467.88804               0.44852               0.01646               0.80595              -0.23342  

Degrees of Freedom: 1181 Total (i.e. Null);  1177 Residual
Null Deviance:      1390 
Residual Deviance: 1258     AIC: 1268

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6456  -0.9830   0.5993   0.7919   1.6326  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          467.888045  53.325556   8.774  < 2e-16 ***
currentDF[, PROTEIN]   0.448516   0.079848   5.617 1.94e-08 ***
Age                    0.016457   0.007424   2.217   0.0266 *  
Gendermale             0.805951   0.144389   5.582 2.38e-08 ***
ORdate_year           -0.233421   0.026589  -8.779  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1390.3  on 1181  degrees of freedom
Residual deviance: 1258.3  on 1177  degrees of freedom
AIC: 1268.3

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.448516 
Standard error............: 0.079848 
Odds ratio (effect size)..: 1.566 
Lower 95% CI..............: 1.339 
Upper 95% CI..............: 1.831 
Z-value...................: 5.617133 
P-value...................: 1.941519e-08 
Hosmer and Lemeshow r^2...: 0.094988 
Cox and Snell r^2.........: 0.105714 
Nagelkerke's pseudo r^2...: 0.152862 
Sample size of AE DB......: 2423 
Sample size of model......: 1182 
Missing data %............: 51.2175 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale           ORdate_year  
            381.3362                0.1826                0.6048               -0.1900  

Degrees of Freedom: 1178 Total (i.e. Null);  1175 Residual
Null Deviance:      1578 
Residual Deviance: 1483     AIC: 1491

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0163  -1.1800   0.7413   0.9609   1.6978  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          386.982789  46.879353   8.255  < 2e-16 ***
currentDF[, PROTEIN]   0.181469   0.069739   2.602  0.00926 ** 
Age                    0.008978   0.006779   1.324  0.18537    
Gendermale             0.603961   0.134706   4.484 7.34e-06 ***
ORdate_year           -0.193095   0.023380  -8.259  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1578.0  on 1178  degrees of freedom
Residual deviance: 1481.4  on 1174  degrees of freedom
AIC: 1491.4

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.181469 
Standard error............: 0.069739 
Odds ratio (effect size)..: 1.199 
Lower 95% CI..............: 1.046 
Upper 95% CI..............: 1.375 
Z-value...................: 2.602126 
P-value...................: 0.009264778 
Hosmer and Lemeshow r^2...: 0.061186 
Cox and Snell r^2.........: 0.078628 
Nagelkerke's pseudo r^2...: 0.106581 
Sample size of AE DB......: 2423 
Sample size of model......: 1179 
Missing data %............: 51.34131 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale           ORdate_year  
            261.8955                0.2204                0.5260               -0.1306  

Degrees of Freedom: 1175 Total (i.e. Null);  1172 Residual
Null Deviance:      1629 
Residual Deviance: 1571     AIC: 1579

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7993  -1.1493   0.8206   1.0983   1.6704  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          258.120368  44.216573   5.838 5.29e-09 ***
currentDF[, PROTEIN]   0.221792   0.066957   3.312 0.000925 ***
Age                   -0.006995   0.006559  -1.066 0.286220    
Gendermale             0.528275   0.131558   4.016 5.93e-05 ***
ORdate_year           -0.128524   0.022049  -5.829 5.57e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1629.3  on 1175  degrees of freedom
Residual deviance: 1570.2  on 1171  degrees of freedom
AIC: 1580.2

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.221792 
Standard error............: 0.066957 
Odds ratio (effect size)..: 1.248 
Lower 95% CI..............: 1.095 
Upper 95% CI..............: 1.423 
Z-value...................: 3.312458 
P-value...................: 0.0009247989 
Hosmer and Lemeshow r^2...: 0.036292 
Cox and Snell r^2.........: 0.049038 
Nagelkerke's pseudo r^2...: 0.065402 
Sample size of AE DB......: 2423 
Sample size of model......: 1176 
Missing data %............: 51.46513 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             2.83719              -0.30011              -0.02675              -0.29630  

Degrees of Freedom: 1176 Total (i.e. Null);  1173 Residual
Null Deviance:      1470 
Residual Deviance: 1425     AIC: 1433

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9616  -1.3190   0.7505   0.8931   1.3158  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          25.048652  45.710531   0.548 0.583703    
currentDF[, PROTEIN] -0.287159   0.071979  -3.989 6.62e-05 ***
Age                  -0.026390   0.007197  -3.667 0.000246 ***
Gendermale           -0.299149   0.144473  -2.071 0.038395 *  
ORdate_year          -0.011078   0.022797  -0.486 0.626996    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1470.1  on 1176  degrees of freedom
Residual deviance: 1425.2  on 1172  degrees of freedom
AIC: 1435.2

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.287159 
Standard error............: 0.071979 
Odds ratio (effect size)..: 0.75 
Lower 95% CI..............: 0.652 
Upper 95% CI..............: 0.864 
Z-value...................: -3.989465 
P-value...................: 6.622254e-05 
Hosmer and Lemeshow r^2...: 0.030554 
Cox and Snell r^2.........: 0.037444 
Nagelkerke's pseudo r^2...: 0.0525 
Sample size of AE DB......: 2423 
Sample size of model......: 1177 
Missing data %............: 51.42386 

Analysis of MCP1_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  ORdate_year  
  -451.4488       0.2255  

Degrees of Freedom: 555 Total (i.e. Null);  554 Residual
Null Deviance:      749.7 
Residual Deviance: 741.7    AIC: 745.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6532  -1.2833   0.8799   1.0256   1.3391  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -404.18119  165.52876  -2.442   0.0146 *
currentDF[, PROTEIN]   -0.09186    0.09068  -1.013   0.3110  
Age                     0.01195    0.01016   1.176   0.2397  
Gendermale             -0.15600    0.19729  -0.791   0.4291  
ORdate_year             0.20155    0.08263   2.439   0.0147 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 749.67  on 555  degrees of freedom
Residual deviance: 738.30  on 551  degrees of freedom
AIC: 748.3

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.091864 
Standard error............: 0.090683 
Odds ratio (effect size)..: 0.912 
Lower 95% CI..............: 0.764 
Upper 95% CI..............: 1.09 
Z-value...................: -1.013025 
P-value...................: 0.3110484 
Hosmer and Lemeshow r^2...: 0.015167 
Cox and Snell r^2.........: 0.020242 
Nagelkerke's pseudo r^2...: 0.027342 
Sample size of AE DB......: 2423 
Sample size of model......: 556 
Missing data %............: 77.05324 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    ORdate_year, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]           ORdate_year  
           -780.9134               -0.4799                0.3905  

Degrees of Freedom: 553 Total (i.e. Null);  551 Residual
Null Deviance:      538 
Residual Deviance: 498  AIC: 504

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3527   0.3688   0.5145   0.6775   1.4039  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -801.64188  214.85023  -3.731 0.000191 ***
currentDF[, PROTEIN]   -0.48686    0.12206  -3.989 6.65e-05 ***
Age                    -0.01852    0.01355  -1.367 0.171532    
Gendermale             -0.14801    0.26231  -0.564 0.572575    
ORdate_year             0.40152    0.10726   3.743 0.000182 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 537.98  on 553  degrees of freedom
Residual deviance: 495.64  on 549  degrees of freedom
AIC: 505.64

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.486864 
Standard error............: 0.122064 
Odds ratio (effect size)..: 0.615 
Lower 95% CI..............: 0.484 
Upper 95% CI..............: 0.781 
Z-value...................: -3.988597 
P-value...................: 6.646533e-05 
Hosmer and Lemeshow r^2...: 0.0787 
Cox and Snell r^2.........: 0.073577 
Nagelkerke's pseudo r^2...: 0.118419 
Sample size of AE DB......: 2423 
Sample size of model......: 554 
Missing data %............: 77.13578 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
              1.2197                0.6668                0.5508  

Degrees of Freedom: 555 Total (i.e. Null);  553 Residual
Null Deviance:      538.8 
Residual Deviance: 497.2    AIC: 503.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4515   0.3661   0.5131   0.6573   1.4042  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -2.478e+02  2.145e+02  -1.156    0.248    
currentDF[, PROTEIN]  6.953e-01  1.238e-01   5.617 1.94e-08 ***
Age                   4.338e-03  1.299e-02   0.334    0.738    
Gendermale            5.263e-01  2.365e-01   2.226    0.026 *  
ORdate_year           1.242e-01  1.071e-01   1.160    0.246    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 538.82  on 555  degrees of freedom
Residual deviance: 495.66  on 551  degrees of freedom
AIC: 505.66

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.695328 
Standard error............: 0.123786 
Odds ratio (effect size)..: 2.004 
Lower 95% CI..............: 1.573 
Upper 95% CI..............: 2.555 
Z-value...................: 5.617164 
P-value...................: 1.941174e-08 
Hosmer and Lemeshow r^2...: 0.080102 
Cox and Snell r^2.........: 0.07469 
Nagelkerke's pseudo r^2...: 0.120356 
Sample size of AE DB......: 2423 
Sample size of model......: 556 
Missing data %............: 77.05324 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
   -0.76646      0.02073      0.78990  

Degrees of Freedom: 555 Total (i.e. Null);  553 Residual
Null Deviance:      611.8 
Residual Deviance: 594.4    AIC: 600.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0029   0.5582   0.6468   0.7206   1.1969  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)           15.339755 191.148455   0.080 0.936038    
currentDF[, PROTEIN]   0.064053   0.104480   0.613 0.539831    
Age                    0.021375   0.011622   1.839 0.065905 .  
Gendermale             0.774693   0.212151   3.652 0.000261 ***
ORdate_year           -0.008053   0.095422  -0.084 0.932743    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 611.78  on 555  degrees of freedom
Residual deviance: 593.99  on 551  degrees of freedom
AIC: 603.99

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.064053 
Standard error............: 0.10448 
Odds ratio (effect size)..: 1.066 
Lower 95% CI..............: 0.869 
Upper 95% CI..............: 1.308 
Z-value...................: 0.613068 
P-value...................: 0.5398311 
Hosmer and Lemeshow r^2...: 0.029089 
Cox and Snell r^2.........: 0.031501 
Nagelkerke's pseudo r^2...: 0.047211 
Sample size of AE DB......: 2423 
Sample size of model......: 556 
Missing data %............: 77.05324 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale           ORdate_year  
           -823.9069                0.3857                0.3390                0.4112  

Degrees of Freedom: 551 Total (i.e. Null);  548 Residual
Null Deviance:      749.1 
Residual Deviance: 711.3    AIC: 719.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9353  -1.1973   0.7687   1.0163   1.6249  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -839.30600  175.54024  -4.781 1.74e-06 ***
currentDF[, PROTEIN]    0.37956    0.09495   3.998 6.40e-05 ***
Age                    -0.01358    0.01043  -1.302   0.1928    
Gendermale              0.34867    0.19858   1.756   0.0791 .  
ORdate_year             0.41935    0.08763   4.785 1.71e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 749.15  on 551  degrees of freedom
Residual deviance: 709.62  on 547  degrees of freedom
AIC: 719.62

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.379557 
Standard error............: 0.094948 
Odds ratio (effect size)..: 1.462 
Lower 95% CI..............: 1.213 
Upper 95% CI..............: 1.761 
Z-value...................: 3.997506 
P-value...................: 6.401333e-05 
Hosmer and Lemeshow r^2...: 0.052768 
Cox and Snell r^2.........: 0.06911 
Nagelkerke's pseudo r^2...: 0.093064 
Sample size of AE DB......: 2423 
Sample size of model......: 552 
Missing data %............: 77.21832 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale           ORdate_year  
          -331.25779              -0.44085              -0.03875              -0.59679               0.16731  

Degrees of Freedom: 552 Total (i.e. Null);  548 Residual
Null Deviance:      667.1 
Residual Deviance: 622.7    AIC: 632.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1704  -1.2187   0.6567   0.8334   1.4336  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -331.25779  184.63912  -1.794  0.07280 .  
currentDF[, PROTEIN]   -0.44085    0.10541  -4.182 2.88e-05 ***
Age                    -0.03875    0.01183  -3.276  0.00105 ** 
Gendermale             -0.59679    0.23370  -2.554  0.01066 *  
ORdate_year             0.16731    0.09218   1.815  0.06952 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 667.10  on 552  degrees of freedom
Residual deviance: 622.68  on 548  degrees of freedom
AIC: 632.68

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.440853 
Standard error............: 0.105406 
Odds ratio (effect size)..: 0.643 
Lower 95% CI..............: 0.523 
Upper 95% CI..............: 0.791 
Z-value...................: -4.182437 
P-value...................: 2.88401e-05 
Hosmer and Lemeshow r^2...: 0.066596 
Cox and Snell r^2.........: 0.077195 
Nagelkerke's pseudo r^2...: 0.110168 
Sample size of AE DB......: 2423 
Sample size of model......: 553 
Missing data %............: 77.17705 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ml_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale                ORdate_year  Hypertension.compositeyes  
               -260.49868                    0.06069                    0.29422                    0.12984                   -0.13333  
        Med.Statin.LLDyes  
                 -0.21052  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.90020 -0.57577 -0.02735  0.60041  3.00874 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -2.635e+02  2.097e+01 -12.563  < 2e-16 ***
currentDF[, TRAIT]         5.919e-02  2.995e-02   1.977  0.04835 *  
Age                        1.825e-03  3.597e-03   0.507  0.61205    
Gendermale                 3.196e-01  6.437e-02   4.964  8.1e-07 ***
ORdate_year                1.313e-01  1.046e-02  12.553  < 2e-16 ***
Hypertension.compositeyes -1.348e-01  8.759e-02  -1.539  0.12424    
DiabetesStatusDiabetes    -4.051e-02  7.024e-02  -0.577  0.56426    
SmokerStatusEx-smoker     -5.669e-02  6.636e-02  -0.854  0.39319    
SmokerStatusNever smoked   2.783e-02  9.370e-02   0.297  0.76651    
Med.Statin.LLDyes         -2.103e-01  7.096e-02  -2.964  0.00311 ** 
Med.all.antiplateletyes    6.384e-02  9.880e-02   0.646  0.51833    
GFR_MDRD                  -4.344e-04  1.529e-03  -0.284  0.77647    
BMI                       -2.830e-03  8.022e-03  -0.353  0.72432    
MedHx_CVDyes               5.212e-03  6.028e-02   0.086  0.93111    
stenose50-70%             -1.761e-01  3.922e-01  -0.449  0.65353    
stenose70-90%              4.823e-03  3.765e-01   0.013  0.98978    
stenose90-99%             -3.883e-02  3.770e-01  -0.103  0.91800    
stenose100% (Occlusion)   -2.378e-01  4.841e-01  -0.491  0.62344    
stenose50-99%             -4.599e-01  5.907e-01  -0.779  0.43637    
stenose70-99%             -3.348e-01  5.299e-01  -0.632  0.52770    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.911 on 1001 degrees of freedom
Multiple R-squared:  0.1751,    Adjusted R-squared:  0.1595 
F-statistic: 11.19 on 19 and 1001 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.059193 
Standard error............: 0.029945 
Odds ratio (effect size)..: 1.061 
Lower 95% CI..............: 1.001 
Upper 95% CI..............: 1.125 
T-value...................: 1.976704 
P-value...................: 0.04834925 
R^2.......................: 0.175135 
Adjusted r^2..............: 0.159478 
Sample size of AE DB......: 2423 
Sample size of model......: 1021 
Missing data %............: 57.86215 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale                ORdate_year  Hypertension.compositeyes  
               -235.47131                   -0.09684                    0.26854                    0.11737                   -0.13276  
        Med.Statin.LLDyes  
                 -0.19071  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.06717 -0.59118 -0.01529  0.56640  3.08389 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -2.401e+02  2.070e+01 -11.601  < 2e-16 ***
currentDF[, TRAIT]        -9.359e-02  3.061e-02  -3.058  0.00229 ** 
Age                       -8.914e-05  3.618e-03  -0.025  0.98035    
Gendermale                 2.930e-01  6.519e-02   4.494  7.8e-06 ***
ORdate_year                1.197e-01  1.032e-02  11.598  < 2e-16 ***
Hypertension.compositeyes -1.246e-01  8.751e-02  -1.424  0.15478    
DiabetesStatusDiabetes    -4.013e-02  7.016e-02  -0.572  0.56745    
SmokerStatusEx-smoker     -5.388e-02  6.638e-02  -0.812  0.41716    
SmokerStatusNever smoked   2.007e-02  9.357e-02   0.214  0.83020    
Med.Statin.LLDyes         -1.952e-01  7.081e-02  -2.756  0.00595 ** 
Med.all.antiplateletyes    4.912e-02  9.867e-02   0.498  0.61872    
GFR_MDRD                  -1.574e-04  1.530e-03  -0.103  0.91809    
BMI                       -3.457e-03  8.022e-03  -0.431  0.66658    
MedHx_CVDyes               2.805e-03  6.029e-02   0.047  0.96290    
stenose50-70%             -1.347e-01  3.917e-01  -0.344  0.73094    
stenose70-90%              5.330e-02  3.760e-01   0.142  0.88731    
stenose90-99%              7.963e-03  3.766e-01   0.021  0.98313    
stenose100% (Occlusion)   -2.181e-01  4.833e-01  -0.451  0.65191    
stenose50-99%             -3.620e-01  5.902e-01  -0.613  0.53980    
stenose70-99%             -2.212e-01  5.293e-01  -0.418  0.67615    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9094 on 997 degrees of freedom
Multiple R-squared:  0.1788,    Adjusted R-squared:  0.1631 
F-statistic: 11.42 on 19 and 997 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.093585 
Standard error............: 0.030608 
Odds ratio (effect size)..: 0.911 
Lower 95% CI..............: 0.858 
Upper 95% CI..............: 0.967 
T-value...................: -3.057532 
P-value...................: 0.002291186 
R^2.......................: 0.17877 
Adjusted r^2..............: 0.16312 
Sample size of AE DB......: 2423 
Sample size of model......: 1017 
Missing data %............: 58.02724 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale                ORdate_year  Hypertension.compositeyes  
                -257.0356                     0.1286                     0.2537                     0.1281                    -0.1341  
        Med.Statin.LLDyes  
                  -0.2143  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.87932 -0.58271 -0.01362  0.55751  3.02886 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -2.621e+02  2.028e+01 -12.925  < 2e-16 ***
currentDF[, TRAIT]         1.256e-01  2.973e-02   4.227 2.59e-05 ***
Age                        1.042e-03  3.587e-03   0.291  0.77144    
Gendermale                 2.771e-01  6.516e-02   4.252 2.32e-05 ***
ORdate_year                1.306e-01  1.011e-02  12.916  < 2e-16 ***
Hypertension.compositeyes -1.291e-01  8.719e-02  -1.481  0.13889    
DiabetesStatusDiabetes    -3.981e-02  6.990e-02  -0.570  0.56914    
SmokerStatusEx-smoker     -5.634e-02  6.623e-02  -0.851  0.39520    
SmokerStatusNever smoked   2.452e-03  9.355e-02   0.026  0.97910    
Med.Statin.LLDyes         -2.159e-01  7.072e-02  -3.053  0.00232 ** 
Med.all.antiplateletyes    6.327e-02  9.826e-02   0.644  0.51976    
GFR_MDRD                  -2.742e-04  1.523e-03  -0.180  0.85713    
BMI                       -3.173e-03  8.000e-03  -0.397  0.69171    
MedHx_CVDyes              -4.603e-03  6.019e-02  -0.076  0.93906    
stenose50-70%             -1.642e-01  3.904e-01  -0.421  0.67409    
stenose70-90%              2.382e-02  3.744e-01   0.064  0.94928    
stenose90-99%             -4.335e-03  3.750e-01  -0.012  0.99078    
stenose100% (Occlusion)   -1.729e-01  4.818e-01  -0.359  0.71968    
stenose50-99%             -4.007e-01  5.875e-01  -0.682  0.49537    
stenose70-99%             -2.931e-01  5.268e-01  -0.556  0.57804    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.906 on 996 degrees of freedom
Multiple R-squared:  0.1857,    Adjusted R-squared:  0.1701 
F-statistic: 11.95 on 19 and 996 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.125649 
Standard error............: 0.029729 
Odds ratio (effect size)..: 1.134 
Lower 95% CI..............: 1.07 
Upper 95% CI..............: 1.202 
T-value...................: 4.226533 
P-value...................: 2.591157e-05 
R^2.......................: 0.185682 
Adjusted r^2..............: 0.170148 
Sample size of AE DB......: 2423 
Sample size of model......: 1016 
Missing data %............: 58.06851 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale                ORdate_year  Hypertension.compositeyes  
               -232.26066                   -0.07525                    0.29943                    0.11576                   -0.12715  
        Med.Statin.LLDyes  
                 -0.21061  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.96576 -0.59822 -0.00485  0.58684  3.01447 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -2.349e+02  2.150e+01 -10.925  < 2e-16 ***
currentDF[, TRAIT]        -7.728e-02  3.096e-02  -2.496  0.01273 *  
Age                        1.892e-03  3.757e-03   0.504  0.61463    
Gendermale                 3.218e-01  6.710e-02   4.796 1.88e-06 ***
ORdate_year                1.171e-01  1.073e-02  10.912  < 2e-16 ***
Hypertension.compositeyes -1.342e-01  9.157e-02  -1.466  0.14306    
DiabetesStatusDiabetes    -5.770e-02  7.505e-02  -0.769  0.44220    
SmokerStatusEx-smoker     -4.766e-02  6.953e-02  -0.685  0.49321    
SmokerStatusNever smoked   7.042e-03  9.807e-02   0.072  0.94278    
Med.Statin.LLDyes         -2.113e-01  7.346e-02  -2.877  0.00411 ** 
Med.all.antiplateletyes    7.706e-02  1.049e-01   0.735  0.46265    
GFR_MDRD                  -6.724e-04  1.607e-03  -0.418  0.67581    
BMI                       -2.815e-04  8.387e-03  -0.034  0.97323    
MedHx_CVDyes               1.458e-02  6.296e-02   0.232  0.81686    
stenose50-70%             -2.957e-01  4.327e-01  -0.683  0.49451    
stenose70-90%             -5.852e-02  4.163e-01  -0.141  0.88824    
stenose90-99%             -1.188e-01  4.164e-01  -0.285  0.77550    
stenose100% (Occlusion)   -3.299e-01  5.170e-01  -0.638  0.52349    
stenose50-99%             -3.890e-01  6.203e-01  -0.627  0.53068    
stenose70-99%             -5.688e-01  6.212e-01  -0.916  0.36010    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9202 on 933 degrees of freedom
Multiple R-squared:   0.17, Adjusted R-squared:  0.1531 
F-statistic: 10.06 on 19 and 933 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.077283 
Standard error............: 0.030963 
Odds ratio (effect size)..: 0.926 
Lower 95% CI..............: 0.871 
Upper 95% CI..............: 0.984 
T-value...................: -2.496001 
P-value...................: 0.01273189 
R^2.......................: 0.169991 
Adjusted r^2..............: 0.153088 
Sample size of AE DB......: 2423 
Sample size of model......: 953 
Missing data %............: 60.66859 

Analysis of MCP1_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_year + Hypertension.composite, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale                ORdate_year  Hypertension.compositeyes  
                 441.5668                     0.1036                     0.2776                    -0.2203                    -0.2432  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3192 -0.6253  0.0206  0.6596  2.6344 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                4.154e+02  8.330e+01   4.987 8.60e-07 ***
currentDF[, TRAIT]         9.996e-02  4.118e-02   2.427  0.01558 *  
Age                       -9.280e-03  5.771e-03  -1.608  0.10849    
Gendermale                 3.003e-01  1.002e-01   2.997  0.00287 ** 
ORdate_year               -2.067e-01  4.157e-02  -4.972 9.23e-07 ***
Hypertension.compositeyes -2.382e-01  1.329e-01  -1.791  0.07385 .  
DiabetesStatusDiabetes    -6.961e-02  1.124e-01  -0.619  0.53601    
SmokerStatusEx-smoker      8.372e-02  9.983e-02   0.839  0.40209    
SmokerStatusNever smoked   2.684e-01  1.476e-01   1.819  0.06960 .  
Med.Statin.LLDyes         -1.509e-01  1.035e-01  -1.457  0.14568    
Med.all.antiplateletyes    1.368e-01  1.587e-01   0.862  0.38929    
GFR_MDRD                  -1.657e-04  2.489e-03  -0.067  0.94696    
BMI                       -1.297e-02  1.190e-02  -1.090  0.27621    
MedHx_CVDyes               2.265e-02  9.344e-02   0.242  0.80855    
stenose50-70%             -4.499e-01  6.185e-01  -0.727  0.46738    
stenose70-90%             -2.733e-01  5.744e-01  -0.476  0.63444    
stenose90-99%             -2.510e-01  5.728e-01  -0.438  0.66144    
stenose100% (Occlusion)   -9.705e-01  7.264e-01  -1.336  0.18217    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9725 on 479 degrees of freedom
Multiple R-squared:  0.1108,    Adjusted R-squared:  0.0792 
F-statistic:  3.51 on 17 and 479 DF,  p-value: 3.139e-06

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.099963 
Standard error............: 0.041184 
Odds ratio (effect size)..: 1.105 
Lower 95% CI..............: 1.019 
Upper 95% CI..............: 1.198 
T-value...................: 2.427249 
P-value...................: 0.01558152 
R^2.......................: 0.110762 
Adjusted r^2..............: 0.079203 
Sample size of AE DB......: 2423 
Sample size of model......: 497 
Missing data %............: 79.48824 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale                ORdate_year  
                511.09348                   -0.22506                   -0.01132                    0.23728                   -0.25465  
Hypertension.compositeyes  
                 -0.19903  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1076 -0.6197 -0.0034  0.6938  2.4632 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                4.972e+02  8.244e+01   6.031 3.26e-09 ***
currentDF[, TRAIT]        -2.277e-01  4.248e-02  -5.361 1.29e-07 ***
Age                       -1.495e-02  5.694e-03  -2.626  0.00891 ** 
Gendermale                 2.430e-01  9.865e-02   2.463  0.01411 *  
ORdate_year               -2.474e-01  4.114e-02  -6.013 3.63e-09 ***
Hypertension.compositeyes -2.009e-01  1.285e-01  -1.563  0.11877    
DiabetesStatusDiabetes    -7.173e-02  1.095e-01  -0.655  0.51256    
SmokerStatusEx-smoker      1.214e-01  9.718e-02   1.249  0.21228    
SmokerStatusNever smoked   2.460e-01  1.435e-01   1.714  0.08712 .  
Med.Statin.LLDyes         -1.412e-01  1.011e-01  -1.397  0.16307    
Med.all.antiplateletyes    1.136e-01  1.545e-01   0.735  0.46259    
GFR_MDRD                   2.431e-05  2.423e-03   0.010  0.99200    
BMI                       -1.152e-02  1.157e-02  -0.996  0.31990    
MedHx_CVDyes               1.966e-02  9.120e-02   0.216  0.82941    
stenose50-70%             -3.893e-01  6.021e-01  -0.647  0.51816    
stenose70-90%             -2.757e-01  5.591e-01  -0.493  0.62215    
stenose90-99%             -2.863e-01  5.573e-01  -0.514  0.60768    
stenose100% (Occlusion)   -1.135e+00  7.069e-01  -1.605  0.10909    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9465 on 477 degrees of freedom
Multiple R-squared:  0.1508,    Adjusted R-squared:  0.1206 
F-statistic: 4.983 on 17 and 477 DF,  p-value: 5.352e-10

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.22772 
Standard error............: 0.042481 
Odds ratio (effect size)..: 0.796 
Lower 95% CI..............: 0.733 
Upper 95% CI..............: 0.865 
T-value...................: -5.360554 
P-value...................: 1.29467e-07 
R^2.......................: 0.150815 
Adjusted r^2..............: 0.12055 
Sample size of AE DB......: 2423 
Sample size of model......: 495 
Missing data %............: 79.57078 

- processing MAC_SMC_ratio_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + Med.Statin.LLD, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale                ORdate_year  
               467.386591                   0.217255                  -0.009929                   0.229963                  -0.232837  
Hypertension.compositeyes          Med.Statin.LLDyes  
                -0.219314                  -0.151354  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2086 -0.6173 -0.0234  0.6782  2.6562 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                4.666e+02  8.141e+01   5.732 1.77e-08 ***
currentDF[, TRAIT]         2.178e-01  3.972e-02   5.485 6.72e-08 ***
Age                       -1.198e-02  5.637e-03  -2.125   0.0341 *  
Gendermale                 2.435e-01  9.849e-02   2.472   0.0138 *  
ORdate_year               -2.322e-01  4.063e-02  -5.714 1.94e-08 ***
Hypertension.compositeyes -2.375e-01  1.292e-01  -1.838   0.0666 .  
DiabetesStatusDiabetes    -6.732e-02  1.092e-01  -0.617   0.5378    
SmokerStatusEx-smoker      7.512e-02  9.692e-02   0.775   0.4387    
SmokerStatusNever smoked   2.284e-01  1.434e-01   1.593   0.1119    
Med.Statin.LLDyes         -1.495e-01  1.010e-01  -1.480   0.1394    
Med.all.antiplateletyes    1.235e-01  1.541e-01   0.801   0.4235    
GFR_MDRD                   2.685e-04  2.420e-03   0.111   0.9117    
BMI                       -1.346e-02  1.155e-02  -1.165   0.2445    
MedHx_CVDyes              -1.570e-02  9.116e-02  -0.172   0.8633    
stenose50-70%             -3.589e-01  6.008e-01  -0.597   0.5505    
stenose70-90%             -3.064e-01  5.577e-01  -0.549   0.5830    
stenose90-99%             -2.365e-01  5.560e-01  -0.425   0.6708    
stenose100% (Occlusion)   -1.056e+00  7.048e-01  -1.499   0.1346    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.944 on 476 degrees of freedom
Multiple R-squared:  0.155, Adjusted R-squared:  0.1248 
F-statistic: 5.135 on 17 and 476 DF,  p-value: 2.17e-10

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.217845 
Standard error............: 0.039715 
Odds ratio (effect size)..: 1.243 
Lower 95% CI..............: 1.15 
Upper 95% CI..............: 1.344 
T-value...................: 5.485155 
P-value...................: 6.719875e-08 
R^2.......................: 0.154968 
Adjusted r^2..............: 0.124788 
Sample size of AE DB......: 2423 
Sample size of model......: 494 
Missing data %............: 79.61205 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)

Coefficients:
(Intercept)   Gendermale  ORdate_year  
   463.7343       0.3103      -0.2315  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3250 -0.6474  0.0106  0.6218  2.5297 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                4.218e+02  8.620e+01   4.893 1.37e-06 ***
currentDF[, TRAIT]        -4.850e-02  5.471e-02  -0.887   0.3758    
Age                       -9.513e-03  5.860e-03  -1.623   0.1052    
Gendermale                 3.285e-01  1.016e-01   3.235   0.0013 ** 
ORdate_year               -2.100e-01  4.302e-02  -4.880 1.46e-06 ***
Hypertension.compositeyes -1.645e-01  1.355e-01  -1.214   0.2254    
DiabetesStatusDiabetes    -3.384e-02  1.148e-01  -0.295   0.7683    
SmokerStatusEx-smoker      9.358e-02  1.014e-01   0.923   0.3567    
SmokerStatusNever smoked   2.664e-01  1.497e-01   1.780   0.0758 .  
Med.Statin.LLDyes         -1.516e-01  1.052e-01  -1.442   0.1500    
Med.all.antiplateletyes    1.357e-01  1.616e-01   0.840   0.4015    
GFR_MDRD                   6.123e-04  2.560e-03   0.239   0.8111    
BMI                       -1.108e-02  1.208e-02  -0.917   0.3595    
MedHx_CVDyes               3.668e-02  9.504e-02   0.386   0.6997    
stenose50-70%             -5.382e-01  6.214e-01  -0.866   0.3869    
stenose70-90%             -2.824e-01  5.775e-01  -0.489   0.6251    
stenose90-99%             -2.830e-01  5.758e-01  -0.491   0.6233    
stenose100% (Occlusion)   -1.043e+00  7.302e-01  -1.429   0.1538    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9771 on 469 degrees of freedom
Multiple R-squared:  0.1035,    Adjusted R-squared:  0.07105 
F-statistic: 3.186 on 17 and 469 DF,  p-value: 2.002e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.048502 
Standard error............: 0.054711 
Odds ratio (effect size)..: 0.953 
Lower 95% CI..............: 0.856 
Upper 95% CI..............: 1.06 
T-value...................: -0.886513 
P-value...................: 0.3757955 
R^2.......................: 0.103542 
Adjusted r^2..............: 0.071048 
Sample size of AE DB......: 2423 
Sample size of model......: 487 
Missing data %............: 79.90095 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of MCP1_pg_ml_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + ORdate_year + SmokerStatus, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                       Age               ORdate_year     SmokerStatusEx-smoker  
               279.63668                  -0.39476                   0.02869                  -0.14023                  -0.41699  
SmokerStatusNever smoked  
                -0.46274  

Degrees of Freedom: 1025 Total (i.e. Null);  1020 Residual
Null Deviance:      1420 
Residual Deviance: 1308     AIC: 1320

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.863  -1.045  -0.608   1.074   2.130  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               257.844061  50.214433   5.135 2.82e-07 ***
currentDF[, PROTEIN]       -0.402700   0.075598  -5.327 9.99e-08 ***
Age                         0.029437   0.008526   3.453 0.000555 ***
Gendermale                 -0.003628   0.150954  -0.024 0.980824    
ORdate_year                -0.129597   0.025058  -5.172 2.32e-07 ***
Hypertension.compositeyes   0.283761   0.205758   1.379 0.167863    
DiabetesStatusDiabetes     -0.231758   0.164388  -1.410 0.158591    
SmokerStatusEx-smoker      -0.428649   0.155564  -2.755 0.005861 ** 
SmokerStatusNever smoked   -0.502689   0.218425  -2.301 0.021368 *  
Med.Statin.LLDyes          -0.024746   0.165034  -0.150 0.880809    
Med.all.antiplateletyes    -0.052996   0.228424  -0.232 0.816534    
GFR_MDRD                    0.001570   0.003596   0.437 0.662445    
BMI                         0.021001   0.018720   1.122 0.261931    
MedHx_CVDyes               -0.040669   0.140089  -0.290 0.771581    
stenose50-70%              -0.823178   0.929921  -0.885 0.376042    
stenose70-90%              -0.353208   0.888418  -0.398 0.690948    
stenose90-99%              -0.317638   0.889813  -0.357 0.721113    
stenose100% (Occlusion)     0.802490   1.222297   0.657 0.511475    
stenose50-99%             -14.187677 432.155336  -0.033 0.973810    
stenose70-99%              -0.451708   1.232581  -0.366 0.714012    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1420.3  on 1025  degrees of freedom
Residual deviance: 1294.3  on 1006  degrees of freedom
AIC: 1334.3

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.4027 
Standard error............: 0.075598 
Odds ratio (effect size)..: 0.669 
Lower 95% CI..............: 0.576 
Upper 95% CI..............: 0.775 
Z-value...................: -5.32687 
P-value...................: 9.991985e-08 
Hosmer and Lemeshow r^2...: 0.088671 
Cox and Snell r^2.........: 0.115512 
Nagelkerke's pseudo r^2...: 0.154119 
Sample size of AE DB......: 2423 
Sample size of model......: 1026 
Missing data %............: 57.6558 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    ORdate_year + SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]               ORdate_year     SmokerStatusEx-smoker  SmokerStatusNever smoked  
               -82.29156                  -0.31120                   0.04123                  -0.38704                  -0.66237  
                     BMI              MedHx_CVDyes  
                 0.04011                   0.24788  

Degrees of Freedom: 1026 Total (i.e. Null);  1020 Residual
Null Deviance:      1049 
Residual Deviance: 1021     AIC: 1035

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3321   0.4334   0.6127   0.7234   1.1601  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -3.689e+01  9.545e+02  -0.039 0.969169    
currentDF[, PROTEIN]      -3.104e-01  8.728e-02  -3.556 0.000377 ***
Age                        1.523e-02  9.880e-03   1.541 0.123267    
Gendermale                 3.495e-02  1.787e-01   0.196 0.844926    
ORdate_year                2.524e-02  2.928e-02   0.862 0.388654    
Hypertension.compositeyes  2.493e-01  2.289e-01   1.089 0.276119    
DiabetesStatusDiabetes     7.073e-02  1.979e-01   0.358 0.720716    
SmokerStatusEx-smoker     -4.601e-01  1.902e-01  -2.419 0.015552 *  
SmokerStatusNever smoked  -7.831e-01  2.492e-01  -3.142 0.001676 ** 
Med.Statin.LLDyes         -8.771e-04  1.935e-01  -0.005 0.996383    
Med.all.antiplateletyes    2.678e-01  2.604e-01   1.029 0.303619    
GFR_MDRD                   5.127e-03  4.253e-03   1.206 0.227994    
BMI                        4.255e-02  2.333e-02   1.824 0.068186 .  
MedHx_CVDyes               2.191e-01  1.637e-01   1.339 0.180703    
stenose50-70%             -1.490e+01  9.527e+02  -0.016 0.987519    
stenose70-90%             -1.519e+01  9.527e+02  -0.016 0.987282    
stenose90-99%             -1.529e+01  9.527e+02  -0.016 0.987198    
stenose100% (Occlusion)    3.121e-02  1.235e+03   0.000 0.999980    
stenose50-99%             -2.727e-01  1.512e+03   0.000 0.999856    
stenose70-99%             -1.484e+01  9.527e+02  -0.016 0.987573    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1048.5  on 1026  degrees of freedom
Residual deviance: 1006.6  on 1007  degrees of freedom
AIC: 1046.6

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.310382 
Standard error............: 0.087283 
Odds ratio (effect size)..: 0.733 
Lower 95% CI..............: 0.618 
Upper 95% CI..............: 0.87 
Z-value...................: -3.55603 
P-value...................: 0.0003765011 
Hosmer and Lemeshow r^2...: 0.040027 
Cox and Snell r^2.........: 0.040043 
Nagelkerke's pseudo r^2...: 0.06259 
Sample size of AE DB......: 2423 
Sample size of model......: 1027 
Missing data %............: 57.61453 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + SmokerStatus, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                       Age                Gendermale               ORdate_year  
               469.37418                   0.45540                   0.01347                   0.86049                  -0.23404  
   SmokerStatusEx-smoker  SmokerStatusNever smoked  
                -0.29641                   0.29609  

Degrees of Freedom: 1026 Total (i.e. Null);  1020 Residual
Null Deviance:      1209 
Residual Deviance: 1092     AIC: 1106

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6326  -0.9680   0.5857   0.7829   1.6994  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               484.672877 355.498773   1.363   0.1728    
currentDF[, PROTEIN]        0.447618   0.086377   5.182 2.19e-07 ***
Age                         0.015842   0.009283   1.707   0.0879 .  
Gendermale                  0.870259   0.163696   5.316 1.06e-07 ***
ORdate_year                -0.235162   0.030057  -7.824 5.12e-15 ***
Hypertension.compositeyes  -0.053017   0.230251  -0.230   0.8179    
DiabetesStatusDiabetes     -0.183150   0.181689  -1.008   0.3134    
SmokerStatusEx-smoker      -0.316266   0.174529  -1.812   0.0700 .  
SmokerStatusNever smoked    0.288237   0.255876   1.126   0.2600    
Med.Statin.LLDyes          -0.049076   0.191425  -0.256   0.7977    
Med.all.antiplateletyes     0.096033   0.259188   0.371   0.7110    
GFR_MDRD                    0.001989   0.003985   0.499   0.6176    
BMI                         0.005657   0.020516   0.276   0.7828    
MedHx_CVDyes                0.093233   0.157184   0.593   0.5531    
stenose50-70%             -13.345325 350.353303  -0.038   0.9696    
stenose70-90%             -13.480479 350.353191  -0.038   0.9693    
stenose90-99%             -13.551667 350.353198  -0.039   0.9691    
stenose100% (Occlusion)   -14.180536 350.353926  -0.040   0.9677    
stenose50-99%             -14.930710 350.355179  -0.043   0.9660    
stenose70-99%             -13.822004 350.354155  -0.039   0.9685    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1209.2  on 1026  degrees of freedom
Residual deviance: 1082.9  on 1007  degrees of freedom
AIC: 1122.9

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.447618 
Standard error............: 0.086377 
Odds ratio (effect size)..: 1.565 
Lower 95% CI..............: 1.321 
Upper 95% CI..............: 1.853 
Z-value...................: 5.182132 
P-value...................: 2.193639e-07 
Hosmer and Lemeshow r^2...: 0.104428 
Cox and Snell r^2.........: 0.115698 
Nagelkerke's pseudo r^2...: 0.167211 
Sample size of AE DB......: 2423 
Sample size of model......: 1027 
Missing data %............: 57.61453 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + ORdate_year + BMI + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale           ORdate_year                   BMI          MedHx_CVDyes  
           392.07230               0.18061               0.50160              -0.19581               0.02969               0.39131  

Degrees of Freedom: 1024 Total (i.e. Null);  1019 Residual
Null Deviance:      1371 
Residual Deviance: 1281     AIC: 1293

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1701  -1.1386   0.6982   0.9617   1.7551  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               413.430677  53.512385   7.726 1.11e-14 ***
currentDF[, PROTEIN]        0.180008   0.075716   2.377 0.017434 *  
Age                         0.010033   0.008479   1.183 0.236689    
Gendermale                  0.545667   0.152016   3.590 0.000331 ***
ORdate_year                -0.206531   0.026700  -7.735 1.03e-14 ***
Hypertension.compositeyes  -0.112636   0.207286  -0.543 0.586867    
DiabetesStatusDiabetes     -0.113506   0.165535  -0.686 0.492907    
SmokerStatusEx-smoker      -0.097149   0.158542  -0.613 0.540032    
SmokerStatusNever smoked   -0.141723   0.218025  -0.650 0.515672    
Med.Statin.LLDyes          -0.086084   0.170254  -0.506 0.613122    
Med.all.antiplateletyes     0.103732   0.232980   0.445 0.656148    
GFR_MDRD                   -0.002822   0.003613  -0.781 0.434817    
BMI                         0.036544   0.019035   1.920 0.054876 .  
MedHx_CVDyes                0.365201   0.141153   2.587 0.009674 ** 
stenose50-70%              -0.401826   0.943263  -0.426 0.670111    
stenose70-90%              -0.391222   0.909306  -0.430 0.667020    
stenose90-99%              -0.317648   0.911397  -0.349 0.727443    
stenose100% (Occlusion)    -0.745875   1.137193  -0.656 0.511894    
stenose50-99%               0.123165   1.360810   0.091 0.927883    
stenose70-99%               1.975403   1.425834   1.385 0.165919    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1371.2  on 1024  degrees of freedom
Residual deviance: 1268.6  on 1005  degrees of freedom
AIC: 1308.6

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.180008 
Standard error............: 0.075716 
Odds ratio (effect size)..: 1.197 
Lower 95% CI..............: 1.032 
Upper 95% CI..............: 1.389 
Z-value...................: 2.377427 
P-value...................: 0.0174339 
Hosmer and Lemeshow r^2...: 0.074795 
Cox and Snell r^2.........: 0.095211 
Nagelkerke's pseudo r^2...: 0.12909 
Sample size of AE DB......: 2423 
Sample size of model......: 1025 
Missing data %............: 57.69707 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + ORdate_year + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                Gendermale               ORdate_year     SmokerStatusEx-smoker  
               275.73528                   0.22784                   0.50126                  -0.13759                   0.05857  
SmokerStatusNever smoked         Med.Statin.LLDyes   Med.all.antiplateletyes  
                 0.42075                   0.40047                  -0.33729  

Degrees of Freedom: 1022 Total (i.e. Null);  1015 Residual
Null Deviance:      1417 
Residual Deviance: 1359     AIC: 1375

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.903  -1.130   0.754   1.097   1.650  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               296.254795  50.487265   5.868 4.41e-09 ***
currentDF[, PROTEIN]        0.230423   0.072546   3.176 0.001492 ** 
Age                        -0.009277   0.008169  -1.136 0.256081    
Gendermale                  0.507404   0.147625   3.437 0.000588 ***
ORdate_year                -0.146960   0.025176  -5.837 5.30e-09 ***
Hypertension.compositeyes  -0.018654   0.198510  -0.094 0.925134    
DiabetesStatusDiabetes     -0.028077   0.159451  -0.176 0.860227    
SmokerStatusEx-smoker       0.113143   0.150717   0.751 0.452835    
SmokerStatusNever smoked    0.509672   0.214258   2.379 0.017370 *  
Med.Statin.LLDyes           0.378901   0.161396   2.348 0.018892 *  
Med.all.antiplateletyes    -0.419125   0.227897  -1.839 0.065901 .  
GFR_MDRD                    0.001073   0.003467   0.310 0.756838    
BMI                        -0.016377   0.018281  -0.896 0.370346    
MedHx_CVDyes                0.147434   0.136619   1.079 0.280517    
stenose50-70%              -0.680770   0.923420  -0.737 0.460985    
stenose70-90%              -0.715597   0.889289  -0.805 0.421002    
stenose90-99%              -0.798488   0.890683  -0.896 0.369991    
stenose100% (Occlusion)    -1.742277   1.149497  -1.516 0.129599    
stenose50-99%              -0.131148   1.354973  -0.097 0.922893    
stenose70-99%               0.356396   1.186652   0.300 0.763919    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1417.5  on 1022  degrees of freedom
Residual deviance: 1350.2  on 1003  degrees of freedom
AIC: 1390.2

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.230423 
Standard error............: 0.072546 
Odds ratio (effect size)..: 1.259 
Lower 95% CI..............: 1.092 
Upper 95% CI..............: 1.452 
Z-value...................: 3.176236 
P-value...................: 0.001491997 
Hosmer and Lemeshow r^2...: 0.047428 
Cox and Snell r^2.........: 0.063603 
Nagelkerke's pseudo r^2...: 0.084824 
Sample size of AE DB......: 2423 
Sample size of model......: 1023 
Missing data %............: 57.77961 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + SmokerStatus, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]                       Age                Gendermale     SmokerStatusEx-smoker  
                 2.86041                  -0.28454                  -0.02413                  -0.37184                  -0.06239  
SmokerStatusNever smoked  
                -0.42455  

Degrees of Freedom: 1022 Total (i.e. Null);  1017 Residual
Null Deviance:      1260 
Residual Deviance: 1218     AIC: 1230

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0646  -1.2798   0.7265   0.8731   1.3692  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -21.133458  52.552076  -0.402 0.687579    
currentDF[, PROTEIN]       -0.284484   0.078214  -3.637 0.000276 ***
Age                        -0.021624   0.009007  -2.401 0.016362 *  
Gendermale                 -0.400820   0.164887  -2.431 0.015062 *  
ORdate_year                 0.011380   0.026223   0.434 0.664310    
Hypertension.compositeyes   0.176586   0.212257   0.832 0.405441    
DiabetesStatusDiabetes      0.007316   0.171510   0.043 0.965977    
SmokerStatusEx-smoker      -0.030192   0.165026  -0.183 0.854833    
SmokerStatusNever smoked   -0.416898   0.222200  -1.876 0.060624 .  
Med.Statin.LLDyes           0.020226   0.171804   0.118 0.906284    
Med.all.antiplateletyes    -0.121723   0.238580  -0.510 0.609914    
GFR_MDRD                    0.005240   0.003756   1.395 0.163024    
BMI                        -0.002086   0.020132  -0.104 0.917473    
MedHx_CVDyes               -0.055130   0.148005  -0.372 0.709529    
stenose50-70%               0.281200   0.883275   0.318 0.750211    
stenose70-90%               0.529893   0.845581   0.627 0.530881    
stenose90-99%               0.833372   0.847648   0.983 0.325530    
stenose100% (Occlusion)     0.459584   1.110889   0.414 0.679088    
stenose50-99%              14.334778 428.022224   0.033 0.973283    
stenose70-99%               0.328315   1.165197   0.282 0.778122    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1260.0  on 1022  degrees of freedom
Residual deviance: 1204.9  on 1003  degrees of freedom
AIC: 1244.9

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.284484 
Standard error............: 0.078214 
Odds ratio (effect size)..: 0.752 
Lower 95% CI..............: 0.645 
Upper 95% CI..............: 0.877 
Z-value...................: -3.637273 
P-value...................: 0.0002755402 
Hosmer and Lemeshow r^2...: 0.043715 
Cox and Snell r^2.........: 0.052418 
Nagelkerke's pseudo r^2...: 0.074016 
Sample size of AE DB......: 2423 
Sample size of model......: 1023 
Missing data %............: 57.77961 

Analysis of MCP1_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ ORdate_year + DiabetesStatus + 
    GFR_MDRD + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)             ORdate_year  DiabetesStatusDiabetes                GFR_MDRD            MedHx_CVDyes  
            -5.235e+02               2.619e-01              -4.535e-01              -9.264e-03              -3.696e-01  

Degrees of Freedom: 497 Total (i.e. Null);  493 Residual
Null Deviance:      675.4 
Residual Deviance: 656.8    AIC: 666.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7543  -1.2079   0.8134   1.0099   1.6284  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -5.303e+02  1.846e+02  -2.872  0.00408 **
currentDF[, PROTEIN]      -9.703e-02  9.764e-02  -0.994  0.32037   
Age                        1.444e-03  1.244e-02   0.116  0.90761   
Gendermale                -6.259e-02  2.178e-01  -0.287  0.77380   
ORdate_year                2.642e-01  9.214e-02   2.868  0.00414 **
Hypertension.compositeyes  3.948e-01  2.795e-01   1.413  0.15772   
DiabetesStatusDiabetes    -5.263e-01  2.397e-01  -2.196  0.02808 * 
SmokerStatusEx-smoker     -1.944e-01  2.133e-01  -0.911  0.36216   
SmokerStatusNever smoked  -9.473e-02  3.207e-01  -0.295  0.76768   
Med.Statin.LLDyes         -1.919e-01  2.227e-01  -0.861  0.38897   
Med.all.antiplateletyes    2.845e-01  3.414e-01   0.833  0.40477   
GFR_MDRD                  -9.088e-03  5.390e-03  -1.686  0.09180 . 
BMI                        1.066e-02  2.573e-02   0.414  0.67859   
MedHx_CVDyes              -3.417e-01  2.018e-01  -1.693  0.09049 . 
stenose50-70%              1.416e+00  1.348e+00   1.050  0.29354   
stenose70-90%              1.678e+00  1.259e+00   1.333  0.18247   
stenose90-99%              1.412e+00  1.254e+00   1.126  0.26026   
stenose100% (Occlusion)    1.621e+00  1.604e+00   1.011  0.31222   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 675.45  on 497  degrees of freedom
Residual deviance: 648.38  on 480  degrees of freedom
AIC: 684.38

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.097028 
Standard error............: 0.097644 
Odds ratio (effect size)..: 0.908 
Lower 95% CI..............: 0.749 
Upper 95% CI..............: 1.099 
Z-value...................: -0.993689 
P-value...................: 0.3203742 
Hosmer and Lemeshow r^2...: 0.04007 
Cox and Snell r^2.........: 0.052897 
Nagelkerke's pseudo r^2...: 0.071252 
Sample size of AE DB......: 2423 
Sample size of model......: 498 
Missing data %............: 79.44697 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    ORdate_year + SmokerStatus + Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)      currentDF[, PROTEIN]               ORdate_year     SmokerStatusEx-smoker  SmokerStatusNever smoked  
               -800.8702                   -0.5054                    0.4003                   -0.5831                   -0.9310  
 Med.all.antiplateletyes  
                  0.7596  

Degrees of Freedom: 495 Total (i.e. Null);  490 Residual
Null Deviance:      493.1 
Residual Deviance: 447.1    AIC: 459.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3991   0.2993   0.4921   0.6733   1.3455  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -7.590e+02  8.436e+02  -0.900 0.368269    
currentDF[, PROTEIN]      -4.958e-01  1.301e-01  -3.812 0.000138 ***
Age                       -5.619e-03  1.617e-02  -0.347 0.728273    
Gendermale                -1.393e-01  2.861e-01  -0.487 0.626335    
ORdate_year                3.866e-01  1.171e-01   3.302 0.000961 ***
Hypertension.compositeyes  2.724e-01  3.500e-01   0.778 0.436329    
DiabetesStatusDiabetes     1.981e-01  3.234e-01   0.612 0.540222    
SmokerStatusEx-smoker     -5.865e-01  2.850e-01  -2.058 0.039624 *  
SmokerStatusNever smoked  -9.821e-01  3.912e-01  -2.510 0.012068 *  
Med.Statin.LLDyes         -9.019e-02  2.759e-01  -0.327 0.743778    
Med.all.antiplateletyes    8.581e-01  4.047e-01   2.120 0.033973 *  
GFR_MDRD                  -2.424e-03  7.057e-03  -0.344 0.731196    
BMI                       -9.001e-03  3.494e-02  -0.258 0.796738    
MedHx_CVDyes               7.111e-03  2.589e-01   0.027 0.978092    
stenose50-70%             -1.253e+01  8.103e+02  -0.015 0.987662    
stenose70-90%             -1.352e+01  8.103e+02  -0.017 0.986683    
stenose90-99%             -1.400e+01  8.103e+02  -0.017 0.986217    
stenose100% (Occlusion)   -1.323e+01  8.103e+02  -0.016 0.986975    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 493.05  on 495  degrees of freedom
Residual deviance: 439.04  on 478  degrees of freedom
AIC: 475.04

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.495808 
Standard error............: 0.130055 
Odds ratio (effect size)..: 0.609 
Lower 95% CI..............: 0.472 
Upper 95% CI..............: 0.786 
Z-value...................: -3.812285 
P-value...................: 0.0001376877 
Hosmer and Lemeshow r^2...: 0.10955 
Cox and Snell r^2.........: 0.103179 
Nagelkerke's pseudo r^2...: 0.163795 
Sample size of AE DB......: 2423 
Sample size of model......: 496 
Missing data %............: 79.52951 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite + SmokerStatus, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes      SmokerStatusEx-smoker  
                   0.8092                     0.6602                     0.6928                     0.6592                    -0.6083  
 SmokerStatusNever smoked  
                   0.1413  

Degrees of Freedom: 497 Total (i.e. Null);  492 Residual
Null Deviance:      491.1 
Residual Deviance: 444.9    AIC: 456.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5848   0.3017   0.4903   0.6709   1.8103  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -3.019e+02  8.644e+02  -0.349   0.7269    
currentDF[, PROTEIN]       6.900e-01  1.329e-01   5.190 2.11e-07 ***
Age                        2.784e-03  1.595e-02   0.175   0.8614    
Gendermale                 6.538e-01  2.651e-01   2.467   0.0136 *  
ORdate_year                1.572e-01  1.183e-01   1.328   0.1841    
Hypertension.compositeyes  6.489e-01  3.426e-01   1.894   0.0582 .  
DiabetesStatusDiabetes    -3.041e-01  2.996e-01  -1.015   0.3102    
SmokerStatusEx-smoker     -6.573e-01  2.800e-01  -2.347   0.0189 *  
SmokerStatusNever smoked   4.369e-02  4.552e-01   0.096   0.9235    
Med.Statin.LLDyes         -2.220e-01  2.967e-01  -0.748   0.4545    
Med.all.antiplateletyes    2.654e-01  4.143e-01   0.641   0.5218    
GFR_MDRD                   1.690e-03  7.138e-03   0.237   0.8128    
BMI                        3.527e-02  3.295e-02   1.070   0.2844    
MedHx_CVDyes               1.223e-01  2.549e-01   0.480   0.6313    
stenose50-70%             -1.438e+01  8.312e+02  -0.017   0.9862    
stenose70-90%             -1.327e+01  8.312e+02  -0.016   0.9873    
stenose90-99%             -1.361e+01  8.312e+02  -0.016   0.9869    
stenose100% (Occlusion)   -1.294e+01  8.312e+02  -0.016   0.9876    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 491.11  on 497  degrees of freedom
Residual deviance: 435.50  on 480  degrees of freedom
AIC: 471.5

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.689959 
Standard error............: 0.132948 
Odds ratio (effect size)..: 1.994 
Lower 95% CI..............: 1.536 
Upper 95% CI..............: 2.587 
Z-value...................: 5.1897 
P-value...................: 2.106334e-07 
Hosmer and Lemeshow r^2...: 0.113222 
Cox and Snell r^2.........: 0.105647 
Nagelkerke's pseudo r^2...: 0.168498 
Sample size of AE DB......: 2423 
Sample size of model......: 498 
Missing data %............: 79.44697 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus + BMI + MedHx_CVD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes                     BMI            MedHx_CVDyes  
              -1.99101                 0.01776                 0.74177                -0.50330                 0.05039                 0.34743  

Degrees of Freedom: 497 Total (i.e. Null);  492 Residual
Null Deviance:      552.3 
Residual Deviance: 530.6    AIC: 542.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0930   0.4579   0.6185   0.7655   1.4577  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -44.157516 210.353643  -0.210  0.83373   
currentDF[, PROTEIN]        0.085258   0.112450   0.758  0.44834   
Age                         0.013062   0.014168   0.922  0.35654   
Gendermale                  0.764000   0.235270   3.247  0.00116 **
ORdate_year                 0.020756   0.104972   0.198  0.84326   
Hypertension.compositeyes   0.238795   0.310574   0.769  0.44196   
DiabetesStatusDiabetes     -0.517784   0.264433  -1.958  0.05022 . 
SmokerStatusEx-smoker      -0.079166   0.246460  -0.321  0.74805   
SmokerStatusNever smoked    0.053141   0.367384   0.145  0.88499   
Med.Statin.LLDyes          -0.087080   0.260840  -0.334  0.73850   
Med.all.antiplateletyes    -0.106642   0.399317  -0.267  0.78942   
GFR_MDRD                   -0.005579   0.006259  -0.891  0.37278   
BMI                         0.050000   0.029351   1.704  0.08847 . 
MedHx_CVDyes                0.344835   0.224565   1.536  0.12464   
stenose50-70%               1.271129   1.377715   0.923  0.35620   
stenose70-90%               1.170764   1.265627   0.925  0.35494   
stenose90-99%               1.367189   1.262889   1.083  0.27899   
stenose100% (Occlusion)     1.478726   1.723795   0.858  0.39099   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 552.26  on 497  degrees of freedom
Residual deviance: 526.41  on 480  degrees of freedom
AIC: 562.41

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.085258 
Standard error............: 0.11245 
Odds ratio (effect size)..: 1.089 
Lower 95% CI..............: 0.874 
Upper 95% CI..............: 1.358 
Z-value...................: 0.758181 
P-value...................: 0.4483429 
Hosmer and Lemeshow r^2...: 0.046812 
Cox and Snell r^2.........: 0.050589 
Nagelkerke's pseudo r^2...: 0.075495 
Sample size of AE DB......: 2423 
Sample size of model......: 498 
Missing data %............: 79.44697 

- processing MAC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + ORdate_year + Med.Statin.LLD + GFR_MDRD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale           ORdate_year     Med.Statin.LLDyes              GFR_MDRD  
          -762.20937               0.39750               0.32386               0.38053               0.51246              -0.00836  

Degrees of Freedom: 493 Total (i.e. Null);  488 Residual
Null Deviance:      671.2 
Residual Deviance: 630.4    AIC: 642.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0233  -1.1555   0.7355   0.9909   1.5585  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -7.459e+02  5.422e+02  -1.376 0.168912    
currentDF[, PROTEIN]       3.810e-01  1.016e-01   3.748 0.000178 ***
Age                       -1.768e-02  1.269e-02  -1.393 0.163596    
Gendermale                 3.458e-01  2.195e-01   1.575 0.115240    
ORdate_year                3.798e-01  9.574e-02   3.967 7.28e-05 ***
Hypertension.compositeyes  5.242e-02  2.887e-01   0.182 0.855941    
DiabetesStatusDiabetes    -1.422e-01  2.464e-01  -0.577 0.563990    
SmokerStatusEx-smoker      5.878e-02  2.183e-01   0.269 0.787743    
SmokerStatusNever smoked   1.942e-01  3.266e-01   0.595 0.552050    
Med.Statin.LLDyes          4.288e-01  2.239e-01   1.915 0.055488 .  
Med.all.antiplateletyes   -1.192e-01  3.515e-01  -0.339 0.734579    
GFR_MDRD                  -9.966e-03  5.527e-03  -1.803 0.071365 .  
BMI                       -3.345e-03  2.565e-02  -0.130 0.896243    
MedHx_CVDyes               1.279e-01  2.039e-01   0.627 0.530510    
stenose50-70%             -1.355e+01  5.071e+02  -0.027 0.978679    
stenose70-90%             -1.325e+01  5.071e+02  -0.026 0.979153    
stenose90-99%             -1.355e+01  5.071e+02  -0.027 0.978687    
stenose100% (Occlusion)   -1.393e+01  5.071e+02  -0.027 0.978091    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 671.15  on 493  degrees of freedom
Residual deviance: 623.39  on 476  degrees of freedom
AIC: 659.39

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.380999 
Standard error............: 0.101645 
Odds ratio (effect size)..: 1.464 
Lower 95% CI..............: 1.199 
Upper 95% CI..............: 1.786 
Z-value...................: 3.748326 
P-value...................: 0.0001780186 
Hosmer and Lemeshow r^2...: 0.071171 
Cox and Snell r^2.........: 0.092166 
Nagelkerke's pseudo r^2...: 0.124048 
Sample size of AE DB......: 2423 
Sample size of model......: 494 
Missing data %............: 79.61205 

- processing SMC_binned


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             3.58159              -0.46804              -0.03057              -0.73346  

Degrees of Freedom: 495 Total (i.e. Null);  492 Residual
Null Deviance:      595.8 
Residual Deviance: 558.3    AIC: 566.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3253  -1.1830   0.6182   0.8468   1.4395  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -2.655e+02  5.405e+02  -0.491  0.62331    
currentDF[, PROTEIN]      -4.611e-01  1.126e-01  -4.094 4.23e-05 ***
Age                       -3.691e-02  1.427e-02  -2.587  0.00969 ** 
Gendermale                -8.251e-01  2.669e-01  -3.091  0.00199 ** 
ORdate_year                1.421e-01  1.019e-01   1.395  0.16311    
Hypertension.compositeyes -3.209e-01  3.348e-01  -0.959  0.33780    
DiabetesStatusDiabetes    -2.069e-01  2.620e-01  -0.790  0.42963    
SmokerStatusEx-smoker      2.049e-01  2.393e-01   0.856  0.39174    
SmokerStatusNever smoked  -1.393e-01  3.395e-01  -0.410  0.68158    
Med.Statin.LLDyes         -1.806e-01  2.464e-01  -0.733  0.46353    
Med.all.antiplateletyes   -8.168e-02  3.820e-01  -0.214  0.83070    
GFR_MDRD                  -1.716e-03  5.923e-03  -0.290  0.77209    
BMI                       -2.059e-02  2.927e-02  -0.703  0.48175    
MedHx_CVDyes              -1.088e-01  2.247e-01  -0.484  0.62815    
stenose50-70%             -1.382e+01  5.005e+02  -0.028  0.97797    
stenose70-90%             -1.404e+01  5.005e+02  -0.028  0.97762    
stenose90-99%             -1.391e+01  5.005e+02  -0.028  0.97782    
stenose100% (Occlusion)   -1.486e+01  5.005e+02  -0.030  0.97631    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 595.82  on 495  degrees of freedom
Residual deviance: 547.99  on 478  degrees of freedom
AIC: 583.99

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.461087 
Standard error............: 0.112615 
Odds ratio (effect size)..: 0.631 
Lower 95% CI..............: 0.506 
Upper 95% CI..............: 0.786 
Z-value...................: -4.094379 
P-value...................: 4.233022e-05 
Hosmer and Lemeshow r^2...: 0.080275 
Cox and Snell r^2.........: 0.091928 
Nagelkerke's pseudo r^2...: 0.131479 
Sample size of AE DB......: 2423 
Sample size of model......: 496 
Missing data %............: 79.52951 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque MCP1 levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

  • stroke
  • TIA
  • retinal infarction
  • amaurosis fugax
  • asymptomatic

Model 1

In this model we correct for Age, Gender, and year of surgery.

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of MCP1_pg_ml_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale           ORdate_year  
          -124.83943               0.27031               0.02877              -0.51749               0.06249  

Degrees of Freedom: 1198 Total (i.e. Null);  1194 Residual
Null Deviance:      827.2 
Residual Deviance: 797.3    AIC: 807.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5524   0.3495   0.4339   0.5243   0.8965  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)   
(Intercept)          -124.83943   68.66012  -1.818  0.06903 . 
currentDF[, PROTEIN]    0.27031    0.10237   2.641  0.00828 **
Age                     0.02877    0.01023   2.811  0.00493 **
Gendermale             -0.51749    0.22116  -2.340  0.01929 * 
ORdate_year             0.06249    0.03424   1.825  0.06796 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 827.22  on 1198  degrees of freedom
Residual deviance: 797.31  on 1194  degrees of freedom
AIC: 807.31

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.270307 
Standard error............: 0.102367 
Odds ratio (effect size)..: 1.31 
Lower 95% CI..............: 1.072 
Upper 95% CI..............: 1.602 
Z-value...................: 2.640561 
P-value...................: 0.008276887 
Hosmer and Lemeshow r^2...: 0.036146 
Cox and Snell r^2.........: 0.02463 
Nagelkerke's pseudo r^2...: 0.049419 
Sample size of AE DB......: 2423 
Sample size of model......: 1199 
Missing data %............: 50.51589 

Analysis of MCP1_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    ORdate_year, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]           ORdate_year  
           -473.3895                0.3371                0.2371  

Degrees of Freedom: 555 Total (i.e. Null);  553 Residual
Null Deviance:      479 
Residual Deviance: 468.7    AIC: 474.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3911   0.4414   0.5340   0.6219   1.0452  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)   
(Intercept)          -480.62215  225.42835  -2.132  0.03300 * 
currentDF[, PROTEIN]    0.36235    0.12346   2.935  0.00334 **
Age                     0.01562    0.01370   1.140  0.25440   
Gendermale             -0.29174    0.27407  -1.064  0.28711   
ORdate_year             0.24030    0.11253   2.135  0.03272 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 478.98  on 555  degrees of freedom
Residual deviance: 466.39  on 551  degrees of freedom
AIC: 476.39

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.362354 
Standard error............: 0.123463 
Odds ratio (effect size)..: 1.437 
Lower 95% CI..............: 1.128 
Upper 95% CI..............: 1.83 
Z-value...................: 2.934919 
P-value...................: 0.003336347 
Hosmer and Lemeshow r^2...: 0.026279 
Cox and Snell r^2.........: 0.022385 
Nagelkerke's pseudo r^2...: 0.038764 
Sample size of AE DB......: 2423 
Sample size of model......: 556 
Missing data %............: 77.05324 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of MCP1_pg_ml_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Med.all.antiplatelet + stenose, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale              ORdate_year  
             -145.03274                  0.32511                  0.02147                 -0.48726                  0.08040  
Med.all.antiplateletyes            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               -0.91400                -13.07895                -14.66964                -14.29506                  0.03649  
          stenose50-99%            stenose70-99%  
              -15.84146                 -0.73998  

Degrees of Freedom: 1037 Total (i.e. Null);  1026 Residual
Null Deviance:      726.9 
Residual Deviance: 679.9    AIC: 703.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.2614   0.2818   0.4201   0.5408   1.0245  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -1.486e+02  9.514e+02  -0.156  0.87591   
currentDF[, PROTEIN]       3.063e-01  1.122e-01   2.729  0.00636 **
Age                        2.888e-02  1.278e-02   2.260  0.02385 * 
Gendermale                -4.336e-01  2.409e-01  -1.800  0.07187 . 
ORdate_year                8.214e-02  3.946e-02   2.082  0.03739 * 
Hypertension.compositeyes -3.362e-01  3.458e-01  -0.972  0.33083   
DiabetesStatusDiabetes    -4.766e-02  2.441e-01  -0.195  0.84521   
SmokerStatusEx-smoker     -3.345e-01  2.345e-01  -1.426  0.15373   
SmokerStatusNever smoked  -2.811e-03  3.574e-01  -0.008  0.99372   
Med.Statin.LLDyes         -2.461e-01  2.688e-01  -0.916  0.35983   
Med.all.antiplateletyes   -9.270e-01  4.806e-01  -1.929  0.05372 . 
GFR_MDRD                   6.238e-03  5.532e-03   1.128  0.25950   
BMI                       -8.706e-03  2.805e-02  -0.310  0.75628   
MedHx_CVDyes               9.157e-02  2.110e-01   0.434  0.66436   
stenose50-70%             -1.317e+01  9.481e+02  -0.014  0.98891   
stenose70-90%             -1.473e+01  9.481e+02  -0.016  0.98760   
stenose90-99%             -1.437e+01  9.481e+02  -0.015  0.98791   
stenose100% (Occlusion)   -1.476e-01  1.228e+03   0.000  0.99990   
stenose50-99%             -1.613e+01  9.481e+02  -0.017  0.98642   
stenose70-99%             -7.880e-01  1.183e+03  -0.001  0.99947   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 726.94  on 1037  degrees of freedom
Residual deviance: 673.02  on 1018  degrees of freedom
AIC: 713.02

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.306293 
Standard error............: 0.112242 
Odds ratio (effect size)..: 1.358 
Lower 95% CI..............: 1.09 
Upper 95% CI..............: 1.693 
Z-value...................: 2.728861 
P-value...................: 0.006355351 
Hosmer and Lemeshow r^2...: 0.074179 
Cox and Snell r^2.........: 0.050624 
Nagelkerke's pseudo r^2...: 0.100528 
Sample size of AE DB......: 2423 
Sample size of model......: 1038 
Missing data %............: 57.16054 

Analysis of MCP1_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    ORdate_year + Med.Statin.LLD, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]           ORdate_year     Med.Statin.LLDyes  
           -529.0018                0.3015                0.2650               -0.4436  

Degrees of Freedom: 497 Total (i.e. Null);  494 Residual
Null Deviance:      442.3 
Residual Deviance: 431.4    AIC: 439.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4505   0.3657   0.5162   0.6508   1.2570  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -5.467e+02  1.385e+03  -0.395  0.69306   
currentDF[, PROTEIN]       3.487e-01  1.293e-01   2.697  0.00701 **
Age                        1.924e-02  1.635e-02   1.177  0.23928   
Gendermale                -3.424e-01  2.977e-01  -1.150  0.25001   
ORdate_year                2.808e-01  1.220e-01   2.301  0.02139 * 
Hypertension.compositeyes -5.366e-01  4.410e-01  -1.217  0.22366   
DiabetesStatusDiabetes     1.954e-01  3.251e-01   0.601  0.54780   
SmokerStatusEx-smoker     -1.745e-01  2.861e-01  -0.610  0.54197   
SmokerStatusNever smoked  -4.281e-01  4.090e-01  -1.047  0.29516   
Med.Statin.LLDyes         -3.682e-01  3.131e-01  -1.176  0.23961   
Med.all.antiplateletyes   -4.933e-01  5.140e-01  -0.960  0.33722   
GFR_MDRD                   9.396e-03  7.081e-03   1.327  0.18455   
BMI                        1.197e-02  3.425e-02   0.349  0.72676   
MedHx_CVDyes               8.287e-02  2.647e-01   0.313  0.75424   
stenose50-70%             -1.392e+01  1.363e+03  -0.010  0.99185   
stenose70-90%             -1.531e+01  1.363e+03  -0.011  0.99104   
stenose90-99%             -1.494e+01  1.363e+03  -0.011  0.99126   
stenose100% (Occlusion)   -8.723e-02  1.712e+03   0.000  0.99996   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 442.26  on 497  degrees of freedom
Residual deviance: 417.29  on 480  degrees of freedom
AIC: 453.29

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.348715 
Standard error............: 0.129319 
Odds ratio (effect size)..: 1.417 
Lower 95% CI..............: 1.1 
Upper 95% CI..............: 1.826 
Z-value...................: 2.696545 
P-value...................: 0.007006285 
Hosmer and Lemeshow r^2...: 0.056471 
Cox and Snell r^2.........: 0.048913 
Nagelkerke's pseudo r^2...: 0.083108 
Sample size of AE DB......: 2423 
Sample size of model......: 498 
Missing data %............: 79.44697 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque MCP1 levels and secondary cardiovascular events over a three-year follow-up period.

The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..

The secondary outcomes will be

  • incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: epstroke.3years, these include:
    • cerebral infarction (CVA/stroke)
    • cerebral bleeding (CVA/stroke)
    • fatal cerebral infarction
    • fatal cerebral bleeding.
  • incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: epcoronary.3years, these include:
    • myocardial infarction (MI)
    • coronary angioplasty (PCI/PTCA)
    • cardiovascular death (exact cause to be investigated)
    • coronary bypass (CABG)
    • fatal myocardial infarction (MI)
    • sudden death.
  • cardiovascular death - variable: epcvdeath.3years, these include:
    • cardiovascular death (exact cause to be investigated)
    • fatal myocardial infarction (MI)
    • fatal cerebral infarction
    • fatal cerebral bleeding
    • sudden death
    • fatal heart failure
    • fatal aneurysm rupture
    • other cardiovascular death..

30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.

cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

  • MACE (epmajor.3years)
  • Stroke (epstroke.3years)
  • Coronary events (epcoronary.3years)
  • Cardiovascular death (epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)

Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
* Creating function to summarize Cox regression and prepare container for results.
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.3years"

   0    1 
2035  265 
[1] "Printing the summary of: epstroke.3years"

   0    1 
2171  130 
[1] "Printing the summary of: epcoronary.3years"

   0    1 
2119  182 
[1] "Printing the summary of: epcvdeath.3years"

   0    1 
2210   90 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.710   3.000   2.573   3.000   3.000     125 
[1] "Printing the summary of: ep_stroke_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.877   3.000   2.624   3.000   3.000     125 
[1] "Printing the summary of: ep_coronary_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.784   3.000   2.622   3.000   3.000     125 
[1] "Printing the summary of: ep_cvdeath_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
0.00274 2.91233 3.00000 2.70902 3.00000 3.00000     125 
for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.30days"

   0    1 
2222   78 
[1] "Printing the summary of: epstroke.30days"

   0    1 
2248   53 
[1] "Printing the summary of: epcoronary.30days"

   0    1 
2267   34 
[1] "Printing the summary of: epcvdeath.30days"

   0    1 
2288   12 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.09   30.00   30.00     125 
[1] "Printing the summary of: ep_stroke_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.32   30.00   30.00     125 
[1] "Printing the summary of: ep_coronary_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   30.00   30.00   29.54   30.00   30.00     125 
[1] "Printing the summary of: ep_cvdeath_t_30days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.001  30.000  30.000  29.854  30.000  30.000     125 
for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.90days"

   0    1 
2206   94 
[1] "Printing the summary of: epstroke.90days"

   0    1 
2241   60 
[1] "Printing the summary of: epcoronary.90days"

   0    1 
2257   44 
[1] "Printing the summary of: epcvdeath.90days"

   0    1 
2281   19 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   86.75   90.00   90.00     125 
[1] "Printing the summary of: ep_stroke_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   87.51   90.00   90.00     125 
[1] "Printing the summary of: ep_coronary_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   90.00   90.00   88.21   90.00   90.00     125 
[1] "Printing the summary of: ep_cvdeath_t_90days"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.001  90.000  90.000  89.320  90.000  90.000     125 
for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"

Cox regressions

Let’s perform the actual Cox-regressions. We will apply a couple of models:

  • Model 1: adjusted for age, sex, and year of surgery
  • Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

3 years follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1187, number of events= 140 
   (1236 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]  0.01185   1.01192  0.18373  0.064 0.948575    
Age                                                        0.03489   1.03550  0.01003  3.478 0.000506 ***
Gendermale                                                 0.35203   1.42196  0.20065  1.754 0.079351 .  
ORdate_year                                               -0.02361   0.97667  0.03018 -0.782 0.434149    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]    1.0119     0.9882    0.7059     1.451
Age                                                          1.0355     0.9657    1.0153     1.056
Gendermale                                                   1.4220     0.7033    0.9596     2.107
ORdate_year                                                  0.9767     1.0239    0.9206     1.036

Concordance= 0.589  (se = 0.025 )
Likelihood ratio test= 16.08  on 4 df,   p=0.003
Wald test            = 15.15  on 4 df,   p=0.004
Score (logrank) test = 15.23  on 4 df,   p=0.004


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: 0.01185 
Standard error............: 0.18373 
Odds ratio (effect size)..: 1.012 
Lower 95% CI..............: 0.706 
Upper 95% CI..............: 1.451 
T-value...................: 0.064496 
P-value...................: 0.9485755 
Sample size in model......: 1187 
Number of events..........: 140 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 549, number of events= 70 
   (1874 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.22427   0.79910  0.24647 -0.910   0.3629  
Age                                                        0.02639   1.02674  0.01475  1.789   0.0736 .
Gendermale                                                 0.87183   2.39128  0.34246  2.546   0.0109 *
ORdate_year                                               -0.03519   0.96542  0.11300 -0.311   0.7555  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162]    0.7991     1.2514    0.4929     1.295
Age                                                          1.0267     0.9740    0.9975     1.057
Gendermale                                                   2.3913     0.4182    1.2222     4.679
ORdate_year                                                  0.9654     1.0358    0.7736     1.205

Concordance= 0.618  (se = 0.034 )
Likelihood ratio test= 12.21  on 4 df,   p=0.02
Wald test            = 10.74  on 4 df,   p=0.03
Score (logrank) test = 11.16  on 4 df,   p=0.02


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.224272 
Standard error............: 0.246475 
Odds ratio (effect size)..: 0.799 
Lower 95% CI..............: 0.493 
Upper 95% CI..............: 1.295 
T-value...................: -0.909918 
P-value...................: 0.3628658 
Sample size in model......: 549 
Number of events..........: 70 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1187, number of events= 74 
   (1236 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]  0.05159   1.05295  0.25152  0.205  0.83747   
Age                                                        0.03709   1.03779  0.01382  2.684  0.00728 **
Gendermale                                                 0.09193   1.09629  0.26020  0.353  0.72385   
ORdate_year                                               -0.04704   0.95405  0.04159 -1.131  0.25806   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]     1.053     0.9497    0.6432     1.724
Age                                                           1.038     0.9636    1.0101     1.066
Gendermale                                                    1.096     0.9122    0.6583     1.826
ORdate_year                                                   0.954     1.0482    0.8794     1.035

Concordance= 0.591  (se = 0.035 )
Likelihood ratio test= 8.33  on 4 df,   p=0.08
Wald test            = 7.9  on 4 df,   p=0.1
Score (logrank) test = 7.96  on 4 df,   p=0.09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: 0.051594 
Standard error............: 0.251515 
Odds ratio (effect size)..: 1.053 
Lower 95% CI..............: 0.643 
Upper 95% CI..............: 1.724 
T-value...................: 0.205134 
P-value...................: 0.8374673 
Sample size in model......: 1187 
Number of events..........: 74 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 549, number of events= 36 
   (1874 observations deleted due to missingness)

                                                               coef exp(coef)  se(coef)      z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.366525  0.693139  0.345901 -1.060    0.289
Age                                                        0.007526  1.007554  0.019822  0.380    0.704
Gendermale                                                 0.332937  1.395059  0.403044  0.826    0.409
ORdate_year                                               -0.014799  0.985310  0.157445 -0.094    0.925

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162]    0.6931     1.4427    0.3519     1.365
Age                                                          1.0076     0.9925    0.9692     1.047
Gendermale                                                   1.3951     0.7168    0.6332     3.074
ORdate_year                                                  0.9853     1.0149    0.7237     1.341

Concordance= 0.571  (se = 0.043 )
Likelihood ratio test= 1.96  on 4 df,   p=0.7
Wald test            = 1.92  on 4 df,   p=0.8
Score (logrank) test = 1.93  on 4 df,   p=0.7


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.366525 
Standard error............: 0.345901 
Odds ratio (effect size)..: 0.693 
Lower 95% CI..............: 0.352 
Upper 95% CI..............: 1.365 
T-value...................: -1.059623 
P-value...................: 0.2893161 
Sample size in model......: 549 
Number of events..........: 36 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1187, number of events= 91 
   (1236 observations deleted due to missingness)

                                                               coef exp(coef)  se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]  0.172342  1.188084  0.225648  0.764   0.4450  
Age                                                        0.008689  1.008727  0.012048  0.721   0.4708  
Gendermale                                                 0.643664  1.903442  0.270491  2.380   0.0173 *
ORdate_year                                               -0.055903  0.945631  0.037238 -1.501   0.1333  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]    1.1881     0.8417    0.7634     1.849
Age                                                          1.0087     0.9913    0.9852     1.033
Gendermale                                                   1.9034     0.5254    1.1202     3.234
ORdate_year                                                  0.9456     1.0575    0.8791     1.017

Concordance= 0.591  (se = 0.031 )
Likelihood ratio test= 9.57  on 4 df,   p=0.05
Wald test            = 8.73  on 4 df,   p=0.07
Score (logrank) test = 8.95  on 4 df,   p=0.06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: 0.172342 
Standard error............: 0.225648 
Odds ratio (effect size)..: 1.188 
Lower 95% CI..............: 0.763 
Upper 95% CI..............: 1.849 
T-value...................: 0.763763 
P-value...................: 0.4450084 
Sample size in model......: 1187 
Number of events..........: 91 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 549, number of events= 46 
   (1874 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162]  0.24448   1.27695  0.30911  0.791   0.4290  
Age                                                        0.03668   1.03736  0.01872  1.959   0.0501 .
Gendermale                                                 0.92420   2.51986  0.43913  2.105   0.0353 *
ORdate_year                                               -0.23892   0.78748  0.13604 -1.756   0.0790 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162]    1.2770     0.7831    0.6967     2.340
Age                                                          1.0374     0.9640    1.0000     1.076
Gendermale                                                   2.5199     0.3968    1.0656     5.959
ORdate_year                                                  0.7875     1.2699    0.6032     1.028

Concordance= 0.652  (se = 0.039 )
Likelihood ratio test= 13.98  on 4 df,   p=0.007
Wald test            = 12.67  on 4 df,   p=0.01
Score (logrank) test = 13.17  on 4 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_rank 
Effect size...............: 0.244478 
Standard error............: 0.309109 
Odds ratio (effect size)..: 1.277 
Lower 95% CI..............: 0.697 
Upper 95% CI..............: 2.34 
T-value...................: 0.79091 
P-value...................: 0.4289965 
Sample size in model......: 549 
Number of events..........: 46 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1187, number of events= 45 
   (1236 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] -0.11518   0.89120  0.32234 -0.357   0.7208    
Age                                                        0.09047   1.09469  0.02008  4.505 6.63e-06 ***
Gendermale                                                 0.91435   2.49514  0.41402  2.208   0.0272 *  
ORdate_year                                               -0.06875   0.93356  0.05424 -1.267   0.2050    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]    0.8912     1.1221    0.4738     1.676
Age                                                          1.0947     0.9135    1.0524     1.139
Gendermale                                                   2.4951     0.4008    1.1084     5.617
ORdate_year                                                  0.9336     1.0712    0.8394     1.038

Concordance= 0.716  (se = 0.039 )
Likelihood ratio test= 29.09  on 4 df,   p=7e-06
Wald test            = 24.68  on 4 df,   p=6e-05
Score (logrank) test = 25.41  on 4 df,   p=4e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: -0.115182 
Standard error............: 0.322341 
Odds ratio (effect size)..: 0.891 
Lower 95% CI..............: 0.474 
Upper 95% CI..............: 1.676 
T-value...................: -0.357328 
P-value...................: 0.7208462 
Sample size in model......: 1187 
Number of events..........: 45 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 549, number of events= 26 
   (1874 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.03367   0.96689  0.40417 -0.083   0.9336  
Age                                                        0.05571   1.05729  0.02549  2.185   0.0289 *
Gendermale                                                 1.05290   2.86594  0.61477  1.713   0.0868 .
ORdate_year                                               -0.11039   0.89548  0.18082 -0.611   0.5415  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162]    0.9669     1.0342    0.4379     2.135
Age                                                          1.0573     0.9458    1.0058     1.111
Gendermale                                                   2.8659     0.3489    0.8590     9.562
ORdate_year                                                  0.8955     1.1167    0.6283     1.276

Concordance= 0.679  (se = 0.06 )
Likelihood ratio test= 9.52  on 4 df,   p=0.05
Wald test            = 8.25  on 4 df,   p=0.08
Score (logrank) test = 8.62  on 4 df,   p=0.07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.03367 
Standard error............: 0.40417 
Odds ratio (effect size)..: 0.967 
Lower 95% CI..............: 0.438 
Upper 95% CI..............: 2.135 
T-value...................: -0.083308 
P-value...................: 0.9336068 
Sample size in model......: 549 
Number of events..........: 26 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1029, number of events= 115 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]  1.291e-01  1.138e+00  2.002e-01  0.645 0.518966    
Age                                                        3.304e-02  1.034e+00  1.293e-02  2.556 0.010591 *  
Gendermale                                                 3.709e-01  1.449e+00  2.288e-01  1.621 0.105078    
ORdate_year                                               -1.222e-02  9.879e-01  3.470e-02 -0.352 0.724616    
Hypertension.compositeno                                  -4.257e-01  6.533e-01  3.572e-01 -1.192 0.233306    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.766e-02  9.825e-01  2.237e-01 -0.079 0.937094    
SmokerStatusEx-smoker                                     -5.003e-01  6.063e-01  2.096e-01 -2.387 0.016973 *  
SmokerStatusNever smoked                                  -8.121e-01  4.439e-01  3.418e-01 -2.376 0.017500 *  
Med.Statin.LLDno                                           2.512e-01  1.286e+00  2.183e-01  1.151 0.249766    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     4.271e-01  1.533e+00  2.637e-01  1.620 0.105327    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.926e-02  9.809e-01  4.962e-03 -3.880 0.000104 ***
BMI                                                        5.407e-02  1.056e+00  2.610e-02  2.071 0.038324 *  
MedHx_CVDyes                                               5.365e-01  1.710e+00  2.221e-01  2.416 0.015694 *  
stenose0-49%                                              -1.571e+01  1.504e-07  2.447e+03 -0.006 0.994877    
stenose50-70%                                             -8.674e-01  4.200e-01  8.780e-01 -0.988 0.323168    
stenose70-90%                                             -3.100e-01  7.334e-01  7.471e-01 -0.415 0.678201    
stenose90-99%                                             -2.933e-01  7.458e-01  7.560e-01 -0.388 0.698046    
stenose100% (Occlusion)                                   -1.521e-01  8.589e-01  1.253e+00 -0.121 0.903378    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.531e+01  2.252e-07  2.926e+03 -0.005 0.995826    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.138e+00  8.789e-01   0.76852    1.6846
Age                                                       1.034e+00  9.675e-01   1.00773    1.0601
Gendermale                                                1.449e+00  6.901e-01   0.92531    2.2690
ORdate_year                                               9.879e-01  1.012e+00   0.92291    1.0574
Hypertension.compositeno                                  6.533e-01  1.531e+00   0.32438    1.3157
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.825e-01  1.018e+00   0.63374    1.5232
SmokerStatusEx-smoker                                     6.063e-01  1.649e+00   0.40210    0.9143
SmokerStatusNever smoked                                  4.439e-01  2.253e+00   0.22718    0.8674
Med.Statin.LLDno                                          1.286e+00  7.779e-01   0.83813    1.9719
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.533e+00  6.524e-01   0.91414    2.5702
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.809e-01  1.019e+00   0.97143    0.9905
BMI                                                       1.056e+00  9.474e-01   1.00291    1.1110
MedHx_CVDyes                                              1.710e+00  5.848e-01   1.10656    2.6424
stenose0-49%                                              1.504e-07  6.648e+06   0.00000       Inf
stenose50-70%                                             4.200e-01  2.381e+00   0.07515    2.3477
stenose70-90%                                             7.334e-01  1.363e+00   0.16959    3.1720
stenose90-99%                                             7.458e-01  1.341e+00   0.16947    3.2821
stenose100% (Occlusion)                                   8.589e-01  1.164e+00   0.07373   10.0065
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.252e-07  4.441e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.698  (se = 0.023 )
Likelihood ratio test= 63.82  on 19 df,   p=9e-07
Wald test            = 58.85  on 19 df,   p=6e-06
Score (logrank) test = 62.26  on 19 df,   p=2e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: 0.129126 
Standard error............: 0.200214 
Odds ratio (effect size)..: 1.138 
Lower 95% CI..............: 0.769 
Upper 95% CI..............: 1.685 
T-value...................: 0.64494 
P-value...................: 0.5189661 
Sample size in model......: 1029 
Number of events..........: 115 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 493, number of events= 61 
   (1930 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -3.920e-01  6.757e-01  2.685e-01 -1.460   0.1443  
Age                                                        3.117e-02  1.032e+00  1.813e-02  1.719   0.0857 .
Gendermale                                                 8.127e-01  2.254e+00  3.652e-01  2.226   0.0260 *
ORdate_year                                               -3.205e-02  9.685e-01  1.249e-01 -0.257   0.7975  
Hypertension.compositeno                                  -7.542e-01  4.704e-01  5.309e-01 -1.421   0.1555  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     6.309e-01  1.879e+00  2.951e-01  2.138   0.0325 *
SmokerStatusEx-smoker                                     -6.391e-01  5.278e-01  2.880e-01 -2.219   0.0265 *
SmokerStatusNever smoked                                  -3.224e-01  7.244e-01  4.307e-01 -0.748   0.4542  
Med.Statin.LLDno                                           2.275e-01  1.255e+00  2.962e-01  0.768   0.4426  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                    -1.296e-02  9.871e-01  4.530e-01 -0.029   0.9772  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.091e-02  9.891e-01  6.796e-03 -1.606   0.1083  
BMI                                                        5.177e-03  1.005e+00  3.447e-02  0.150   0.8806  
MedHx_CVDyes                                               5.574e-01  1.746e+00  3.075e-01  1.813   0.0699 .
stenose0-49%                                              -1.651e+01  6.744e-08  3.444e+03 -0.005   0.9962  
stenose50-70%                                             -1.690e+00  1.845e-01  1.448e+00 -1.167   0.2431  
stenose70-90%                                             -7.523e-01  4.713e-01  1.049e+00 -0.717   0.4731  
stenose90-99%                                             -1.050e+00  3.501e-01  1.055e+00 -0.995   0.3198  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 6.757e-01  1.480e+00   0.39920     1.144
Age                                                       1.032e+00  9.693e-01   0.99563     1.069
Gendermale                                                2.254e+00  4.436e-01   1.10189     4.611
ORdate_year                                               9.685e-01  1.033e+00   0.75814     1.237
Hypertension.compositeno                                  4.704e-01  2.126e+00   0.16617     1.332
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.879e+00  5.321e-01   1.05394     3.351
SmokerStatusEx-smoker                                     5.278e-01  1.895e+00   0.30015     0.928
SmokerStatusNever smoked                                  7.244e-01  1.380e+00   0.31142     1.685
Med.Statin.LLDno                                          1.255e+00  7.965e-01   0.70247     2.244
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    9.871e-01  1.013e+00   0.40623     2.399
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.891e-01  1.011e+00   0.97606     1.002
BMI                                                       1.005e+00  9.948e-01   0.93952     1.075
MedHx_CVDyes                                              1.746e+00  5.727e-01   0.95573     3.190
stenose0-49%                                              6.744e-08  1.483e+07   0.00000       Inf
stenose50-70%                                             1.845e-01  5.420e+00   0.01080     3.151
stenose70-90%                                             4.713e-01  2.122e+00   0.06036     3.679
stenose90-99%                                             3.501e-01  2.857e+00   0.04426     2.768
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.703  (se = 0.03 )
Likelihood ratio test= 32.87  on 17 df,   p=0.01
Wald test            = 29.3  on 17 df,   p=0.03
Score (logrank) test = 31.16  on 17 df,   p=0.02


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.392011 
Standard error............: 0.268514 
Odds ratio (effect size)..: 0.676 
Lower 95% CI..............: 0.399 
Upper 95% CI..............: 1.144 
T-value...................: -1.459925 
P-value...................: 0.1443106 
Sample size in model......: 493 
Number of events..........: 61 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1029, number of events= 59 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]  1.594e-01  1.173e+00  2.824e-01  0.564   0.5724  
Age                                                        4.416e-02  1.045e+00  1.793e-02  2.462   0.0138 *
Gendermale                                                -4.998e-02  9.513e-01  3.010e-01 -0.166   0.8681  
ORdate_year                                               -3.475e-02  9.658e-01  4.903e-02 -0.709   0.4785  
Hypertension.compositeno                                  -1.230e-03  9.988e-01  4.192e-01 -0.003   0.9977  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                    -2.173e-02  9.785e-01  3.172e-01 -0.068   0.9454  
SmokerStatusEx-smoker                                     -1.136e-01  8.926e-01  2.965e-01 -0.383   0.7015  
SmokerStatusNever smoked                                  -9.518e-01  3.860e-01  5.240e-01 -1.817   0.0693 .
Med.Statin.LLDno                                           3.482e-01  1.417e+00  2.971e-01  1.172   0.2412  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     3.779e-01  1.459e+00  3.721e-01  1.016   0.3098  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -3.561e-03  9.964e-01  7.027e-03 -0.507   0.6124  
BMI                                                        8.022e-02  1.084e+00  3.436e-02  2.335   0.0195 *
MedHx_CVDyes                                               3.650e-01  1.441e+00  2.941e-01  1.241   0.2146  
stenose0-49%                                              -1.549e+01  1.867e-07  3.367e+03 -0.005   0.9963  
stenose50-70%                                             -6.477e-01  5.233e-01  1.173e+00 -0.552   0.5810  
stenose70-90%                                             -4.535e-01  6.354e-01  1.055e+00 -0.430   0.6673  
stenose90-99%                                             -5.009e-01  6.060e-01  1.070e+00 -0.468   0.6396  
stenose100% (Occlusion)                                    3.518e-01  1.422e+00  1.459e+00  0.241   0.8094  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                             -1.518e+01  2.547e-07  3.975e+03 -0.004   0.9970  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.173e+00  8.526e-01   0.67425     2.040
Age                                                       1.045e+00  9.568e-01   1.00905     1.083
Gendermale                                                9.513e-01  1.051e+00   0.52730     1.716
ORdate_year                                               9.658e-01  1.035e+00   0.87735     1.063
Hypertension.compositeno                                  9.988e-01  1.001e+00   0.43921     2.271
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.785e-01  1.022e+00   0.52549     1.822
SmokerStatusEx-smoker                                     8.926e-01  1.120e+00   0.49923     1.596
SmokerStatusNever smoked                                  3.860e-01  2.590e+00   0.13823     1.078
Med.Statin.LLDno                                          1.417e+00  7.059e-01   0.79124     2.536
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.459e+00  6.853e-01   0.70369     3.026
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.964e-01  1.004e+00   0.98282     1.010
BMI                                                       1.084e+00  9.229e-01   1.01297     1.159
MedHx_CVDyes                                              1.441e+00  6.942e-01   0.80943     2.564
stenose0-49%                                              1.867e-07  5.356e+06   0.00000       Inf
stenose50-70%                                             5.233e-01  1.911e+00   0.05246     5.219
stenose70-90%                                             6.354e-01  1.574e+00   0.08037     5.024
stenose90-99%                                             6.060e-01  1.650e+00   0.07448     4.930
stenose100% (Occlusion)                                   1.422e+00  7.034e-01   0.08147    24.808
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.547e-07  3.926e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.672  (se = 0.034 )
Likelihood ratio test= 23.18  on 19 df,   p=0.2
Wald test            = 21.31  on 19 df,   p=0.3
Score (logrank) test = 22.41  on 19 df,   p=0.3


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: 0.159428 
Standard error............: 0.282444 
Odds ratio (effect size)..: 1.173 
Lower 95% CI..............: 0.674 
Upper 95% CI..............: 2.04 
T-value...................: 0.564459 
P-value...................: 0.5724415 
Sample size in model......: 1029 
Number of events..........: 59 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 493, number of events= 29 
   (1930 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -5.111e-01  5.998e-01  3.922e-01 -1.303    0.192
Age                                                        1.682e-02  1.017e+00  2.487e-02  0.676    0.499
Gendermale                                                 7.422e-02  1.077e+00  4.374e-01  0.170    0.865
ORdate_year                                               -2.613e-02  9.742e-01  1.808e-01 -0.145    0.885
Hypertension.compositeno                                  -8.185e-01  4.411e-01  7.527e-01 -1.087    0.277
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA
DiabetesStatusDiabetes                                     2.130e-01  1.237e+00  4.580e-01  0.465    0.642
SmokerStatusEx-smoker                                     -5.374e-01  5.843e-01  4.196e-01 -1.281    0.200
SmokerStatusNever smoked                                  -3.304e-01  7.186e-01  6.147e-01 -0.537    0.591
Med.Statin.LLDno                                          -6.489e-02  9.372e-01  4.561e-01 -0.142    0.887
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA
Med.all.antiplateletno                                    -8.493e-02  9.186e-01  6.709e-01 -0.127    0.899
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA
GFR_MDRD                                                   2.245e-03  1.002e+00  1.037e-02  0.216    0.829
BMI                                                       -3.808e-03  9.962e-01  4.917e-02 -0.077    0.938
MedHx_CVDyes                                               2.618e-01  1.299e+00  4.162e-01  0.629    0.529
stenose0-49%                                              -1.873e+01  7.320e-09  1.321e+04 -0.001    0.999
stenose50-70%                                             -1.857e+01  8.632e-09  5.072e+03 -0.004    0.997
stenose70-90%                                             -1.307e+00  2.705e-01  1.121e+00 -1.166    0.244
stenose90-99%                                             -1.546e+00  2.130e-01  1.135e+00 -1.362    0.173
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA
stenoseNA                                                         NA         NA  0.000e+00     NA       NA
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA
stenose99                                                         NA         NA  0.000e+00     NA       NA

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 5.998e-01  1.667e+00   0.27811     1.294
Age                                                       1.017e+00  9.833e-01   0.96858     1.068
Gendermale                                                1.077e+00  9.285e-01   0.45700     2.538
ORdate_year                                               9.742e-01  1.026e+00   0.68351     1.389
Hypertension.compositeno                                  4.411e-01  2.267e+00   0.10088     1.929
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.237e+00  8.081e-01   0.50429     3.036
SmokerStatusEx-smoker                                     5.843e-01  1.712e+00   0.25672     1.330
SmokerStatusNever smoked                                  7.186e-01  1.392e+00   0.21540     2.398
Med.Statin.LLDno                                          9.372e-01  1.067e+00   0.38338     2.291
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    9.186e-01  1.089e+00   0.24662     3.421
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.002e+00  9.978e-01   0.98209     1.023
BMI                                                       9.962e-01  1.004e+00   0.90468     1.097
MedHx_CVDyes                                              1.299e+00  7.697e-01   0.57466     2.938
stenose0-49%                                              7.320e-09  1.366e+08   0.00000       Inf
stenose50-70%                                             8.632e-09  1.159e+08   0.00000       Inf
stenose70-90%                                             2.705e-01  3.697e+00   0.03004     2.436
stenose90-99%                                             2.130e-01  4.694e+00   0.02303     1.970
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.651  (se = 0.051 )
Likelihood ratio test= 9.82  on 17 df,   p=0.9
Wald test            = 7.29  on 17 df,   p=1
Score (logrank) test = 9.15  on 17 df,   p=0.9


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.511114 
Standard error............: 0.392158 
Odds ratio (effect size)..: 0.6 
Lower 95% CI..............: 0.278 
Upper 95% CI..............: 1.294 
T-value...................: -1.303338 
P-value...................: 0.1924592 
Sample size in model......: 493 
Number of events..........: 29 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1029, number of events= 78 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]  2.905e-01  1.337e+00  2.410e-01  1.205 0.228034    
Age                                                        3.225e-04  1.000e+00  1.532e-02  0.021 0.983210    
Gendermale                                                 8.268e-01  2.286e+00  3.041e-01  2.719 0.006547 ** 
ORdate_year                                               -4.520e-02  9.558e-01  4.217e-02 -1.072 0.283803    
Hypertension.compositeno                                  -9.704e-01  3.789e-01  5.215e-01 -1.861 0.062793 .  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -8.006e-02  9.231e-01  2.760e-01 -0.290 0.771732    
SmokerStatusEx-smoker                                     -6.194e-01  5.383e-01  2.585e-01 -2.396 0.016592 *  
SmokerStatusNever smoked                                  -2.735e-01  7.607e-01  3.654e-01 -0.748 0.454249    
Med.Statin.LLDno                                           5.528e-02  1.057e+00  2.760e-01  0.200 0.841276    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     3.320e-01  1.394e+00  3.353e-01  0.990 0.322098    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.003e-02  9.802e-01  5.954e-03 -3.365 0.000766 ***
BMI                                                        1.495e-02  1.015e+00  3.351e-02  0.446 0.655552    
MedHx_CVDyes                                               6.941e-01  2.002e+00  2.795e-01  2.483 0.013015 *  
stenose0-49%                                              -1.602e+01  1.106e-07  3.018e+03 -0.005 0.995765    
stenose50-70%                                             -1.801e+00  1.651e-01  1.427e+00 -1.262 0.206807    
stenose70-90%                                             -2.542e-01  7.756e-01  1.043e+00 -0.244 0.807516    
stenose90-99%                                             -3.387e-01  7.127e-01  1.054e+00 -0.321 0.747924    
stenose100% (Occlusion)                                   -1.545e+01  1.953e-07  2.480e+03 -0.006 0.995030    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              7.799e-01  2.181e+00  1.430e+00  0.545 0.585612    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.337e+00  7.479e-01   0.83375    2.1442
Age                                                       1.000e+00  9.997e-01   0.97072    1.0308
Gendermale                                                2.286e+00  4.374e-01   1.25965    4.1490
ORdate_year                                               9.558e-01  1.046e+00   0.87999    1.0382
Hypertension.compositeno                                  3.789e-01  2.639e+00   0.13634    1.0532
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.231e-01  1.083e+00   0.53742    1.5854
SmokerStatusEx-smoker                                     5.383e-01  1.858e+00   0.32429    0.8935
SmokerStatusNever smoked                                  7.607e-01  1.315e+00   0.37170    1.5570
Med.Statin.LLDno                                          1.057e+00  9.462e-01   0.61526    1.8153
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.394e+00  7.175e-01   0.72237    2.6894
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.802e-01  1.020e+00   0.96879    0.9917
BMI                                                       1.015e+00  9.852e-01   0.95054    1.0840
MedHx_CVDyes                                              2.002e+00  4.995e-01   1.15752    3.4620
stenose0-49%                                              1.106e-07  9.039e+06   0.00000       Inf
stenose50-70%                                             1.651e-01  6.056e+00   0.01008    2.7052
stenose70-90%                                             7.756e-01  1.289e+00   0.10037    5.9929
stenose90-99%                                             7.127e-01  1.403e+00   0.09030    5.6244
stenose100% (Occlusion)                                   1.953e-07  5.121e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.181e+00  4.584e-01   0.13215   36.0042
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.733  (se = 0.028 )
Likelihood ratio test= 51.75  on 19 df,   p=7e-05
Wald test            = 45.64  on 19 df,   p=6e-04
Score (logrank) test = 49.2  on 19 df,   p=2e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: 0.290473 
Standard error............: 0.240969 
Odds ratio (effect size)..: 1.337 
Lower 95% CI..............: 0.834 
Upper 95% CI..............: 2.144 
T-value...................: 1.205438 
P-value...................: 0.2280341 
Sample size in model......: 1029 
Number of events..........: 78 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 493, number of events= 42 
   (1930 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162]  3.996e-02  1.041e+00  3.269e-01  0.122   0.9027  
Age                                                        4.142e-02  1.042e+00  2.293e-02  1.806   0.0709 .
Gendermale                                                 9.841e-01  2.675e+00  4.684e-01  2.101   0.0356 *
ORdate_year                                               -2.760e-01  7.588e-01  1.478e-01 -1.867   0.0620 .
Hypertension.compositeno                                  -2.340e-01  7.914e-01  5.431e-01 -0.431   0.6666  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     5.455e-01  1.726e+00  3.576e-01  1.526   0.1271  
SmokerStatusEx-smoker                                     -4.047e-01  6.672e-01  3.492e-01 -1.159   0.2465  
SmokerStatusNever smoked                                  -1.727e-02  9.829e-01  5.071e-01 -0.034   0.9728  
Med.Statin.LLDno                                          -5.702e-02  9.446e-01  3.626e-01 -0.157   0.8751  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     6.030e-01  1.828e+00  4.594e-01  1.313   0.1893  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.491e-02  9.852e-01  8.623e-03 -1.730   0.0837 .
BMI                                                        4.034e-02  1.041e+00  4.216e-02  0.957   0.3387  
MedHx_CVDyes                                               1.532e-01  1.166e+00  3.489e-01  0.439   0.6605  
stenose0-49%                                              -1.499e-01  8.608e-01  8.796e+03  0.000   1.0000  
stenose50-70%                                              1.616e+01  1.041e+07  5.185e+03  0.003   0.9975  
stenose70-90%                                              1.636e+01  1.269e+07  5.185e+03  0.003   0.9975  
stenose90-99%                                              1.621e+01  1.100e+07  5.185e+03  0.003   0.9975  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 1.041e+00  9.608e-01    0.5484     1.975
Age                                                       1.042e+00  9.594e-01    0.9965     1.090
Gendermale                                                2.675e+00  3.738e-01    1.0683     6.700
ORdate_year                                               7.588e-01  1.318e+00    0.5679     1.014
Hypertension.compositeno                                  7.914e-01  1.264e+00    0.2729     2.295
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.726e+00  5.795e-01    0.8562     3.478
SmokerStatusEx-smoker                                     6.672e-01  1.499e+00    0.3365     1.323
SmokerStatusNever smoked                                  9.829e-01  1.017e+00    0.3638     2.655
Med.Statin.LLDno                                          9.446e-01  1.059e+00    0.4641     1.923
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.828e+00  5.472e-01    0.7427     4.497
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.852e-01  1.015e+00    0.9687     1.002
BMI                                                       1.041e+00  9.605e-01    0.9586     1.131
MedHx_CVDyes                                              1.166e+00  8.580e-01    0.5883     2.309
stenose0-49%                                              8.608e-01  1.162e+00    0.0000       Inf
stenose50-70%                                             1.041e+07  9.610e-08    0.0000       Inf
stenose70-90%                                             1.269e+07  7.881e-08    0.0000       Inf
stenose90-99%                                             1.100e+07  9.094e-08    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.725  (se = 0.036 )
Likelihood ratio test= 25.04  on 17 df,   p=0.09
Wald test            = 15.97  on 17 df,   p=0.5
Score (logrank) test = 24.24  on 17 df,   p=0.1


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_rank 
Effect size...............: 0.039956 
Standard error............: 0.326862 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.548 
Upper 95% CI..............: 1.975 
T-value...................: 0.122242 
P-value...................: 0.9027075 
Sample size in model......: 493 
Number of events..........: 42 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [MCP1_pg_ml_2015_rank]; 1 out of 2 proteins.
   > cross tabulation of MCP1_pg_ml_2015_rank-stratum.

[-3.34125,0.00209) [ 0.00209,3.34125] 
               600                599 

   > fitting the model for MCP1_pg_ml_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 1029, number of events= 33 
   (1394 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125]  3.757e-02  1.038e+00  3.794e-01  0.099 0.921123    
Age                                                        7.047e-02  1.073e+00  2.723e-02  2.588 0.009658 ** 
Gendermale                                                 1.226e+00  3.407e+00  5.594e-01  2.191 0.028427 *  
ORdate_year                                               -7.706e-02  9.258e-01  7.153e-02 -1.077 0.281331    
Hypertension.compositeno                                  -1.773e+01  2.000e-08  3.957e+03 -0.004 0.996425    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -9.565e-03  9.905e-01  4.279e-01 -0.022 0.982165    
SmokerStatusEx-smoker                                     -5.440e-01  5.804e-01  4.052e-01 -1.342 0.179449    
SmokerStatusNever smoked                                  -3.778e-01  6.854e-01  6.197e-01 -0.610 0.542134    
Med.Statin.LLDno                                           1.675e-02  1.017e+00  4.225e-01  0.040 0.968375    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.115e+00  3.050e+00  4.178e-01  2.669 0.007602 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.284e-02  9.677e-01  9.422e-03 -3.485 0.000491 ***
BMI                                                        8.583e-02  1.090e+00  5.240e-02  1.638 0.101466    
MedHx_CVDyes                                               7.410e-01  2.098e+00  4.621e-01  1.603 0.108837    
stenose0-49%                                              -2.059e+01  1.144e-09  2.687e+04 -0.001 0.999389    
stenose50-70%                                             -1.271e+00  2.805e-01  1.263e+00 -1.007 0.314004    
stenose70-90%                                             -1.782e+00  1.683e-01  1.122e+00 -1.587 0.112409    
stenose90-99%                                             -1.497e+00  2.239e-01  1.150e+00 -1.301 0.193259    
stenose100% (Occlusion)                                   -1.989e+01  2.301e-09  1.983e+04 -0.001 0.999200    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.943e+01  3.629e-09  3.412e+04 -0.001 0.999546    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.038e+00  9.631e-01   0.49356    2.1842
Age                                                       1.073e+00  9.320e-01   1.01724    1.1318
Gendermale                                                3.407e+00  2.935e-01   1.13818   10.1980
ORdate_year                                               9.258e-01  1.080e+00   0.80473    1.0652
Hypertension.compositeno                                  2.000e-08  5.000e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.905e-01  1.010e+00   0.42820    2.2911
SmokerStatusEx-smoker                                     5.804e-01  1.723e+00   0.26230    1.2843
SmokerStatusNever smoked                                  6.854e-01  1.459e+00   0.20343    2.3092
Med.Statin.LLDno                                          1.017e+00  9.834e-01   0.44427    2.3276
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    3.050e+00  3.278e-01   1.34493    6.9181
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.677e-01  1.033e+00   0.94999    0.9857
BMI                                                       1.090e+00  9.178e-01   0.98326    1.2075
MedHx_CVDyes                                              2.098e+00  4.766e-01   0.84810    5.1899
stenose0-49%                                              1.144e-09  8.743e+08   0.00000       Inf
stenose50-70%                                             2.805e-01  3.566e+00   0.02361    3.3316
stenose70-90%                                             1.683e-01  5.941e+00   0.01865    1.5191
stenose90-99%                                             2.239e-01  4.466e+00   0.02349    2.1340
stenose100% (Occlusion)                                   2.301e-09  4.346e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.629e-09  2.755e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.844  (se = 0.031 )
Likelihood ratio test= 61.1  on 19 df,   p=3e-06
Wald test            = 21.88  on 19 df,   p=0.3
Score (logrank) test = 57.18  on 19 df,   p=1e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ml_2015_rank 
Effect size...............: 0.037572 
Standard error............: 0.37944 
Odds ratio (effect size)..: 1.038 
Lower 95% CI..............: 0.494 
Upper 95% CI..............: 2.184 
T-value...................: 0.099019 
P-value...................: 0.921123 
Sample size in model......: 1029 
Number of events..........: 33 
   > processing [MCP1_rank]; 2 out of 2 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-3.12162,0.00225) [ 0.00225,3.12162] 
               278                278 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 493, number of events= 23 
   (1930 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -2.418e-01  7.852e-01  4.414e-01 -0.548   0.5839  
Age                                                        5.035e-02  1.052e+00  3.206e-02  1.571   0.1163  
Gendermale                                                 1.075e+00  2.930e+00  6.727e-01  1.598   0.1100  
ORdate_year                                               -1.134e-01  8.928e-01  1.951e-01 -0.581   0.5610  
Hypertension.compositeno                                  -1.802e+01  1.487e-08  4.552e+03 -0.004   0.9968  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     5.064e-01  1.659e+00  5.312e-01  0.953   0.3405  
SmokerStatusEx-smoker                                     -6.006e-01  5.485e-01  4.733e-01 -1.269   0.2045  
SmokerStatusNever smoked                                  -1.010e-01  9.039e-01  7.296e-01 -0.138   0.8899  
Med.Statin.LLDno                                           7.451e-01  2.107e+00  4.587e-01  1.625   0.1043  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     5.400e-01  1.716e+00  6.750e-01  0.800   0.4237  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -2.044e-02  9.798e-01  1.039e-02 -1.968   0.0491 *
BMI                                                        2.176e-02  1.022e+00  5.934e-02  0.367   0.7139  
MedHx_CVDyes                                               1.312e+00  3.713e+00  6.416e-01  2.044   0.0409 *
stenose0-49%                                              -7.481e-01  4.733e-01  3.720e+04  0.000   1.0000  
stenose50-70%                                              4.915e-01  1.635e+00  2.257e+04  0.000   1.0000  
stenose70-90%                                              1.839e+01  9.706e+07  2.097e+04  0.001   0.9993  
stenose90-99%                                              1.809e+01  7.220e+07  2.097e+04  0.001   0.9993  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 7.852e-01  1.274e+00    0.3306    1.8651
Age                                                       1.052e+00  9.509e-01    0.9876    1.1199
Gendermale                                                2.930e+00  3.413e-01    0.7839   10.9510
ORdate_year                                               8.928e-01  1.120e+00    0.6091    1.3086
Hypertension.compositeno                                  1.487e-08  6.727e+07    0.0000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.659e+00  6.027e-01    0.5858    4.7001
SmokerStatusEx-smoker                                     5.485e-01  1.823e+00    0.2169    1.3869
SmokerStatusNever smoked                                  9.039e-01  1.106e+00    0.2163    3.7768
Med.Statin.LLDno                                          2.107e+00  4.747e-01    0.8574    5.1763
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.716e+00  5.827e-01    0.4571    6.4433
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.798e-01  1.021e+00    0.9600    0.9999
BMI                                                       1.022e+00  9.785e-01    0.9098    1.1480
MedHx_CVDyes                                              3.713e+00  2.693e-01    1.0557   13.0574
stenose0-49%                                              4.733e-01  2.113e+00    0.0000       Inf
stenose50-70%                                             1.635e+00  6.117e-01    0.0000       Inf
stenose70-90%                                             9.706e+07  1.030e-08    0.0000       Inf
stenose90-99%                                             7.220e+07  1.385e-08    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.815  (se = 0.039 )
Likelihood ratio test= 33.08  on 17 df,   p=0.01
Wald test            = 12.37  on 17 df,   p=0.8
Score (logrank) test = 27.71  on 17 df,   p=0.05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.241771 
Standard error............: 0.441385 
Odds ratio (effect size)..: 0.785 
Lower 95% CI..............: 0.331 
Upper 95% CI..............: 1.865 
T-value...................: -0.547755 
P-value...................: 0.5838601 
Sample size in model......: 493 
Number of events..........: 23 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)
object 'head.style' not found

30-days follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")

write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

90-days follow-up

Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

Correlations

We correlated plaque levels of the biomarkers.

MCP1 plaque levels


# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (ad71a164) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ml_2015_rank",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
str(AEDB.CEA.temp)
'data.frame':   2423 obs. of  15 variables:
 $ MCP1_pg_ml_2015_rank: num  0.45 1.572 0.67 -1.038 0.326 ...
 $ CalcificationPlaque : num  1 1 2 1 2 1 2 1 2 2 ...
 $ CollagenPlaque      : num  2 2 2 2 2 1 2 2 2 1 ...
 $ Fat10Perc           : num  2 2 1 2 2 2 1 2 2 2 ...
 $ IPH                 : num  2 1 2 2 2 2 1 2 2 2 ...
 $ MAC_binned          : num  1 1 1 1 NA 1 1 2 2 1 ...
 $ SMC_binned          : num  1 2 2 2 1 2 2 2 2 1 ...
 $ Macrophages_rank    : num  1.121 0.396 0.29 0.32 -2.316 ...
 $ SMC_rank            : num  1.132 1.27 1.307 0.783 -0.828 ...
 $ MAC_SMC_ratio_rank  : num  0.236 -0.344 -0.42 -0.174 -2.336 ...
 $ VesselDensity_rank  : num  -0.978 1.1 -0.858 -1.068 -0.231 ...
 $ Symptoms.5G         : num  5 6 5 2 6 6 2 6 6 5 ...
 $ AsymptSympt         : num  3 3 3 2 3 3 2 3 3 3 ...
 $ EP_major            : num  0 0 0 1 1 0 0 0 1 1 ...
 $ EP_composite        : num  2 2 2 3 3 2 2 2 3 3 ...
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")
Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
NA
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)

# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ml_2015_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

Session information


Version:      v1.1.0
Last update:  2021-02-11
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_

_S_

_C_


_W_


**Changes log**
* v1.1.0  Fixes needed for compiling the HTML.
* v1.0.19 Updating for different macOS devices. Addressing reviewer comments.
* v1.0.18 Changed 'asymptomatic vs. symptomatic' DotPlot to have dots instead of lines. Added boxplot for the same.
* v1.0.17 Added regular, and per gender boxplots for risk factors, _etc_. Changed coloring for consistency. 
* v1.0.16 Create a pg/mL-only version. Switched to a new .RMD, but kept versioning. Removed the plasma-based analyses.
* v1.0.15 Add sex-stratified plots for MCP1 plaque levels by symptoms and plaque vulnerability index.
* v1.0.14 Add analysis on plasma based MCP1 levels measured through OLINK, n ± 700, limited to symptomatic patients only.
* v1.0.13 Splitting RMDs into plaque-focused, and one including plasma levels of MCP1.
* v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
* v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
* v1.0.10 Add analyses for all three MCP1, MCP1_pg_ml_2015, and MCP1_pg_ug_2015. Add comparison between MCP1, MCP1_pg_ml_2015, and MCP1_pg_ug_2015. Add (and fixed) ordinal regression. Double checked which measurement to use. 
* v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
* v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the MCP1 and MCP1_pg_ug_2015 variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
* v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions.
* v1.0.0 Inital version.

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] GGally_2.1.0               PerformanceAnalytics_2.0.4 xts_0.12.1                 zoo_1.8-8                  ggcorrplot_0.1.3.999      
 [6] Hmisc_4.4-2                Formula_1.2-4              lattice_0.20-41            survminer_0.4.8            survival_3.2-7            
[11] patchwork_1.1.1            ggsci_2.9                  openxlsx_4.2.3             ggpubr_0.4.0               tableone_0.12.0           
[16] labelled_2.7.0             sjPlot_2.8.7               sjlabelled_1.1.7           haven_2.3.1                devtools_2.3.2            
[21] usethis_2.0.0              MASS_7.3-53                DT_0.17                    knitr_1.31                 forcats_0.5.1             
[26] stringr_1.4.0              purrr_0.3.4                tibble_3.0.6               ggplot2_3.3.3              tidyverse_1.3.0           
[31] data.table_1.13.6          naniar_0.6.0               tidyr_1.1.2                dplyr_1.0.4                optparse_1.6.6            
[36] readr_1.4.0               

loaded via a namespace (and not attached):
  [1] readxl_1.3.1        backports_1.2.1     plyr_1.8.6          splines_4.0.3       crosstalk_1.1.1     TH.data_1.0-10     
  [7] digest_0.6.27       htmltools_0.5.1.1   checkmate_2.0.0     magrittr_2.0.1      memoise_2.0.0       cluster_2.1.0      
 [13] remotes_2.2.0       modelr_0.1.8        sandwich_3.0-0      prettyunits_1.1.1   jpeg_0.1-8.1        colorspace_2.0-0   
 [19] rvest_0.3.6         mitools_2.4         xfun_0.20           callr_3.5.1         crayon_1.4.1        jsonlite_1.7.2     
 [25] lme4_1.1-26         glue_1.4.2          gtable_0.3.0        emmeans_1.5.4       sjstats_0.18.1      sjmisc_2.8.6       
 [31] car_3.0-10          pkgbuild_1.2.0      abind_1.4-5         scales_1.1.1        mvtnorm_1.1-1       DBI_1.1.1          
 [37] rstatix_0.6.0       ggeffects_1.0.1     Rcpp_1.0.6          htmlTable_2.1.0     xtable_1.8-4        performance_0.7.0  
 [43] foreign_0.8-81      km.ci_0.5-2         survey_4.0          htmlwidgets_1.5.3   httr_1.4.2          getopt_1.20.3      
 [49] RColorBrewer_1.1-2  ellipsis_0.3.1      reshape_0.8.8       pkgconfig_2.0.3     farver_2.0.3        nnet_7.3-15        
 [55] dbplyr_2.1.0        reshape2_1.4.4      tidyselect_1.1.0    labeling_0.4.2      rlang_0.4.10        effectsize_0.4.3   
 [61] munsell_0.5.0       cellranger_1.1.0    cachem_1.0.3        cli_2.3.0           generics_0.1.0      broom_0.7.4        
 [67] evaluate_0.14       fastmap_1.1.0       yaml_2.2.1          processx_3.4.5      fs_1.5.0            zip_2.1.1          
 [73] survMisc_0.5.5      visdat_0.5.3        nlme_3.1-152        xml2_1.3.2          compiler_4.0.3      rstudioapi_0.13    
 [79] png_0.1-7           curl_4.3            e1071_1.7-4         testthat_3.0.1      ggsignif_0.6.0      reprex_1.0.0       
 [85] statmod_1.4.35      stringi_1.5.3       ps_1.5.0            parameters_0.11.0   desc_1.2.0          Matrix_1.3-2       
 [91] nloptr_1.2.2.2      KMsurv_0.1-5        vctrs_0.3.6         pillar_1.4.7        lifecycle_0.2.0     estimability_1.3   
 [97] insight_0.12.0      latticeExtra_0.6-29 R6_2.5.0            gridExtra_2.3       rio_0.5.16          sessioninfo_1.1.1  
[103] codetools_0.2-18    boot_1.3-26         assertthat_0.2.1    pkgload_1.1.0       rprojroot_2.0.2     withr_2.4.1        
[109] multcomp_1.4-16     mgcv_1.8-33         bayestestR_0.8.2    hms_1.0.0           quadprog_1.5-8      rpart_4.1-15       
[115] grid_4.0.3          coda_0.19-4         class_7.3-18        minqa_1.2.4         rmarkdown_2.6       carData_3.0-4      
[121] base64enc_0.1-3     lubridate_1.7.9.2   tinytex_0.29       

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".main_analyses.RData"))
© 1979-2021 Sander W. van der Laan | s.w.vanderlaan-2[at]gmail.com | swvanderlaan.github.io.
---
title: "Monocyte-chemoattractant protein-1 Levels in Human Atherosclerosis Associate with Plaque Vulnerability."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis, and many others.'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook Pro
# ROOT_loc = "/Users/swvanderlaan"
# GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### MacBook Air
ROOT_loc = "/Users/slaan3"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### GitHub - Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/git/swvanderlaan/2020_georgakis_vanderlaan_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")

PROJECT_loc = paste0(ROOT_loc, "/git/swvanderlaan/2020_georgakis_vanderlaan_MCP1")

### PLINK - Generic Locations
# AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
# LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
# RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
# RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
# 
# PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="MCP1_pg_mL"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

We used the Luminex-platform to measure atherosclerotic plaque proteins. Historically, this was done in two experiments: 


**Experiment 1: **

This entails an experiment where also 20+ other interleukins, cyto- and chemokines, and metalloproteinases were measured. Part of these were measured using LUMINEX, some of them were measured using FACS, ELISA, and activity assays. These assays were run according to instructions from the producer in a research setting. 

- variable `MCP1`: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.


**Experiment 2: **

This entails an experiment where `MCP1` was measured in a clinical diagnostic settings on a clinically validated Luminex-platform. 
- variable `MCP1_pg_ml_2015`: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/mL]. Luminex platform.


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDBraw)
```

## Plaque protein data
Loading Athero-Express plaque protein measurements from 2015.

```{r LoadAE PlaqueProteins}
library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")

names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"

head(AEDB_Protein_2015)

```

## Plasma protein data
Loading Athero-Express plasma protein measurements from 2019/2020 as measured using OLINK.

```{r LoadAE PlasmaProteins OLINK}
library(openxlsx)
AEDB_PlasmaProtein_OLINK_CVD2raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD2_forR")
AEDB_PlasmaProtein_OLINK_CVD3raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD3_forR")
AEDB_PlasmaProtein_OLINK_CMraw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CM_forR")

AEDB_PlasmaProtein_OLINK_ProteinInfo <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/OLINK_ERA_UMC_AE_StroesRentate/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "ProteinInfo")

AEDB_PlasmaProtein_OLINK_CVD2 <- AEDB_PlasmaProtein_OLINK_CVD2raw %>% filter(QC_Warning_CVD2 == "Pass")
AEDB_PlasmaProtein_OLINK_CVD3 <- AEDB_PlasmaProtein_OLINK_CVD3raw %>% filter(QC_Warning_CVD3 == "Pass")
AEDB_PlasmaProtein_OLINK_CM <- AEDB_PlasmaProtein_OLINK_CMraw %>% filter(QC_Warning_CM == "Pass")

table(AEDB_PlasmaProtein_OLINK_CVD2raw$QC_Warning_CVD2)
table(AEDB_PlasmaProtein_OLINK_CVD2$QC_Warning_CVD2)

table(AEDB_PlasmaProtein_OLINK_CVD3raw$QC_Warning_CVD3)
table(AEDB_PlasmaProtein_OLINK_CVD3$QC_Warning_CVD3)

table(AEDB_PlasmaProtein_OLINK_CMraw$QC_Warning_CM)
table(AEDB_PlasmaProtein_OLINK_CM$QC_Warning_CM)

AEDB_PlasmaProtein_OLINK_CVD2$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD2$Order <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Order <- NULL
AEDB_PlasmaProtein_OLINK_CM$Order <- NULL

temp <- merge(AEDB_PlasmaProtein_OLINK_CVD2, AEDB_PlasmaProtein_OLINK_CVD3, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
              sort = FALSE, all.x = TRUE)

AEDB_PlasmaProtein_OLINK <- merge(temp, AEDB_PlasmaProtein_OLINK_CM, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
              sort = FALSE, all.x = TRUE)

AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_cardiometabolic_plt1_29-10-19"] <- "plate 1"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt2"] <- "plate 2"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt3"] <- "plate 3"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt4"] <- "plate 4"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt5"] <- "plate 5"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_pl6"] <- "plate 6"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_CM_plt10"] <- "plate 10"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_plt11_CM"] <- "plate 11"

olink_proteins <- c("BMP6", "ANGPT1", "ADM", "CD40L", "SLAMF7", "PGF", "ADAMTS13", "BOC", "IL4RA", "SRC", "IL1ra", "IL6", "TNFRSF10A", "STK4", "IDUA", 
                    "TNFRSF11A", "PAR1", "TRAILR2", "PRSS27", "TIE2", "TF", "IL1RL2", "PDGF_subunit_B", "IL27", "IL17D", "CXCL1", "LOX1", "Gal9", "GIF", "SCF", 
                    "IL18", "FGF21", "PIgR", "RAGE", "SOD2", "CTRC", "FGF23", "SPON2", "GH", "FS", "GLO1", "CD84", "PAPPA", "SERPINA12", "REN", "DECR1", 
                    "MERTK", "KIM1", "THBS2", "TM", "VSIG2", "AMBP", "PRELP", "HO1", "XCL1", "IL16", "SORT1", "CEACAM8", "PTX3", "PSGL1", "CCL17", "CCL3", 
                    "MMP7", "IgG_Fc_receptor_IIb", "ITGB1BP2", "DCN", "Dkk1", "LPL", "PRSS8", "AGRP", "HBEGF", "GDF2", "FABP2", "THPO", "MARCO", "GT", "BNP", 
                    "MMP12", "ACE2", "PDL2", "CTSL1", "hOSCAR", "TNFRSF13B", "TGM2", "LEP", "CA5A", "HSP_27", "CD4", "NEMO", "VEGFD", "PARP1", "HAOX1", 
                    "TNFRSF14", "LDL_receptor", "ITGB2", "IL17RA", "TNFR2", "MMP9", "EPHB4", "IL2RA", "OPG", "ALCAM", "TFF3", "SELP", "CSTB", "MCP1", "CD163", 
                    "Gal3", "GRN", "NTproBNP", "BLM_hydrolase", "PLC", "LTBR", "Notch_3", "TIMP4", "CNTN1", "CDH5", "TLT2", "FABP4", "TFPI", "PAI", "CCL24", 
                    "TR", "TNFRSF10C", "GDF15", "SELE", "AZU1", "DLK1", "SPON1", "MPO", "CXCL16", "IL6RA", "RETN", "IGFBP1", "CHIT1", "TRAP", "GP6", "PSPD", 
                    "PI3", "EpCAM", "APN", "AXL", "IL1RT1", "MMP2", "FAS", "MB", "TNFSF13B", "PRTN3", "PCSK9", "UPAR", "OPN", "CTSD", "PGLYRP1", "CPA1", "JAMA", 
                    "Gal4", "IL1RT2", "SHPS1", "CCL15", "CASP3", "uPA", "CPB1", "CHI3L1", "ST2", "tPA", "SCGB3A2", "EGFR", "IGFBP7", "CD93", "IL18BP", "COL1A1", 
                    "PON3", "CTSZ", "MMP3", "RARRES2", "ICAM2", "KLK6", "PDGF_subunit_A", "TNFR1", "IGFBP2", "vWF", "PECAM1", "MEPE", "CCL16", "PRCP", "CA1", 
                    "ICAM1", "CHL1", "TGFBI", "ENG", "PLTP", "SERPINA7", "IGFBP3", "CR2", "SERPINA5", "FCGR3B", "IGFBP6", "CDH1", "CCL5", "CCL14", "GNLY", 
                    "NOTCH1", "PAM", "PROC", "CST3", "NCAM1", "PCOLCE", "LILRB1", "MET", "LTBP2", "IL7R", "VCAM1", "SELL", "F11", "COMP", "CA4", "PTPRS", 
                    "MBL2", "TIMP1", "ANGPTL3", "REG3A", "SOD1", "CD46", "ITGAM", "TNC", "NID1", "CFHR5", "SPARCL1", "PLXNB2", "MEGF9", "ANG", "ST6GAL1", 
                    "DPP4", "REG1A", "QPCT", "FCN2", "FETUB", "CES1", "CRTAC1", "TCN2", "PRSS2", "ICAM3", "SAA4", "CNDP1", "FCGR2A", "NRP1", "EFEMP1", "TIMD4", 
                    "FAP", "TIE1", "THBS4", "F7", "GP1BA", "LYVE1", "CA3", "TGFBR3", "DEFA1", "CD59", "APOM", "OSMR", "LILRB2", "UMOD", "CCL18", "COL18A1", 
                    "LCN2", "KIT", "C1QTNF1", "AOC3", "GAS6", "IGLC2", "PLA2G7", "TNXB", "MFAP5", "VASN", "LILRB5", "C2")

length(olink_proteins)

olink_proteins_rank = unlist(lapply(olink_proteins, paste0, "_rankNorm"))

olink_proteins_short <- c("MCP1")
olink_proteins_short_rank <- unlist(lapply(olink_proteins_short, paste0, "_rankNorm"))

rm(temp)

```



### Inspect OLINK data

We know that the proteins are not normally distributed and therefore we will standardise them as follows: 

`z = ( x - μ ) / σ`

Where for each sample, `x` equals the value of the variable, `μ` (_mu_) equals the mean of `x`, and `σ` (_sigma_) equals the standard deviation of `x`.

```{r OLINKStandardize}
for(PROTEIN in 1:length(olink_proteins_short)){
  # AEDB_PlasmaProtein_OLINK$Z <- NULL
  var.temp.z = olink_proteins_short_rank[PROTEIN]
  var.temp = olink_proteins_short[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.z,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  AEDB_PlasmaProtein_OLINK <- AEDB_PlasmaProtein_OLINK %>%
    mutate_each_(funs(as.numeric), olink_proteins_short) 
  
  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])),").\n"))
  
  AEDB_PlasmaProtein_OLINK <- AEDB_PlasmaProtein_OLINK %>%
      mutate_at(vars(var.temp), 
        list(Z = ~ (AEDB_PlasmaProtein_OLINK[,var.temp] - mean(AEDB_PlasmaProtein_OLINK[,var.temp], na.rm = TRUE))/sd(AEDB_PlasmaProtein_OLINK[,var.temp], na.rm = TRUE))
      )
  # str(AEDB_PlasmaProtein_OLINK$Z)
  cat(paste0("* renaming Z to ", var.temp.z,".\n"))
  AEDB_PlasmaProtein_OLINK[,var.temp.z] <- NULL
  names(AEDB_PlasmaProtein_OLINK)[names(AEDB_PlasmaProtein_OLINK) == "Z"] <- var.temp.z
}

rm(var.temp, var.temp.z)
```


Here we summarize some of these data in the subset of genetic data that passed QC.
```{r OLINKSummary}

for(PROTEIN in 1:length(olink_proteins_short)){
  var.temp.z = olink_proteins_short_rank[PROTEIN]
  var.temp = olink_proteins_short[PROTEIN]
  
  cat(paste0("\nSummarising data for ",var.temp," [AU]; n = ",sum(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])),".\n"))
  print(summary(AEDB_PlasmaProtein_OLINK[,var.temp]))
  print(summary(AEDB_PlasmaProtein_OLINK[,var.temp.z]))

}
rm(var.temp, var.temp.z, PROTEIN)

```

```{r OLINKVisualize }
require("ggpubr")
require("ggsci")

# mypal = pal_npg("nrc", alpha = 0.7)(9)
# mypal
# ## [1] "#E64B35B2" "#4DBBD5B2" "#00A087B2" "#3C5488B2" "#F39B7FB2" "#8491B4B2"
# ## [7] "#91D1C2B2" "#DC0000B2" "#7E6148B2"
# library("scales")
# show_col(mypal)

for(PROTEIN in 1:length(olink_proteins_short)){
  # metabolite_unit = ucorbioNMRDataDictionary[ucorbioNMRDataDictionary$Metabolite_NMR == NMRtargets[METABOLITE], "Concentration_NMR"]
  cat(paste0("\nProcessing metabolite [ ",olink_proteins_short[PROTEIN]," (AU)].\n"))

  var.temp = olink_proteins_short[PROTEIN]
  var.temp.z = paste0(olink_proteins_short[PROTEIN],"_rankNorm")
   
  dt.temp <- subset(AEDB_PlasmaProtein_OLINK, select = c("STUDY_NUMBER", var.temp, var.temp.z, "Plate_ID"))
  dt.temp[,2] <- as.numeric(dt.temp[,2])
  
  p1 <- ggpubr::gghistogram(dt.temp %>% filter(!is.na(Plate_ID)),
                    x = var.temp,
                    y = "..count..",
                    color = "#4DBBD5B2", fill = "#4DBBD5B2",
                    # palette = "npg",
                    rug = TRUE,
                    add = "mean",
                    xlab = paste0(var.temp," [AU]."),
                    ggtheme = theme_minimal())
  
  my_comparisons <- list( c("plate 1", "plate 2"), 
                          c("plate 1", "plate 3"), 
                          c("plate 1", "plate 4"), 
                          c("plate 1", "plate 5"), 
                          c("plate 1", "plate 6"), 
                          c("plate 1", "plate 10"), 
                          c("plate 1", "plate 11") )
  p2 <- ggpubr::ggboxplot(data = dt.temp %>% filter(!is.na(Plate_ID)),
                          x = "Plate_ID",
                          y = var.temp.z,
                          color = "Plate_ID",
                          palette = "npg",
                          add = c("mean", "jitter"),
                          # error.plot = "errorbar",
                          xlab = "plates used",
                          ylab = paste0(var.temp.z," [AU]."),
                          # ylim = c(0,4),
                          ggtheme = theme_minimal()) #+
    # stat_compare_means(method = "anova") #+      # Add global p-value
    # stat_compare_means(comparisons = my_comparisons) + # Add pairwise comparisons p-value
    # stat_compare_means(label = "p.signif", method = "t.test", ref.group = "plate 1")
  
  
  p3 <- ggpubr::gghistogram(dt.temp %>% filter(!is.na(Plate_ID)),
                    x = var.temp.z,
                    y = "..count..",
                    color = "#91D1C2B2", fill = "#91D1C2B2",
                    # palette = "npg",
                    rug = TRUE,
                    add = "mean",
                    xlab = paste0(var.temp.z," [AU]."),
                    ggtheme = theme_minimal())
  
  require(patchwork)
  # p4 <- ((p1 / p3 ) | (p2))
  
  p4 <- ggpar(p1, legend = "" ) / ggpar(p2 + rotate_x_text(45), legend = "")  | ggpar(p3, legend = "right")
  
  print(p4)
  ggsave(filename = paste0(QC_loc, "/",Today,".",PROJECTNAME,".OLINK.",var.temp,".png"),
         plot = p4, device = "png", width = 20, height = 20)
  
}
  # rm(my_comparisons,
  #    p1, p2, p3, p4,
  #    var.temp, var.temp.z, dt.temp, PROTEIN)

```


## Merging protein data
We will merge these measurements to the AEDB for comparing pg/ug vs. pg/mL measurements of MCP1 - also in relation to plaque phenotypes. In addition we have more information the experiment and can correct for this.

```{r merge AEDB and Proteins}
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"

temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))

temp2 <- subset(AEDB_PlasmaProtein_OLINK, select = c("STUDY_NUMBER", "MCP1", "MCP1_rankNorm", "Plate_ID"))
names(temp2)[names(temp2) == "MCP1"] <- "MCP1_plasma_olink"
names(temp2)[names(temp2) == "MCP1_rankNorm"] <- "MCP1_plasma_olink_rankNorm"
names(temp2)[names(temp2) == "Plate_ID"] <- "PlateID_plasma_olink"


AEDBraw2 <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)

AEDB <- merge(AEDBraw2, temp2, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)
rm(temp, temp2, AEDBraw2)

temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015",
                                "MCP1_plasma_olink", "MCP1_plasma_olink_rankNorm", "PlateID_plasma_olink"))
dim(temp)
head(temp)
rm(temp)   
```


#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

> Note: There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 

```{r AEDB: describe, message=FALSE, warning=FALSE}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.

```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```



# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments `MCP1`, and `MCP1_pg_ml_2015`)


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

# IL6, IL6R, MCP1 measurements
AEDB$IL6 <- as.numeric(AEDB$IL6) # FACS plaque
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015) # LUMINEX plaque
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1 <- as.numeric(AEDB$MCP1) # LUMINEX plaque
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1_pg_ml_2015 <- as.numeric(AEDB$MCP1_pg_ml_2015) # LUMINEX plaque
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma) # LUMINEX

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6R_pg_ml_2015",
                   "MCP1", "MCP1_pg_ml_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients
Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients with `MCP1_pg_ml_2015`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ml_2015`.

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

### CEA patients with `MCP1_pg_ml_2015` and `MCP1`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ml_2015` _and_ `MCP1`.

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ml_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```


Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

```



## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, and `inverse-rank normal transformation` (standardise). We know that the proteins are not normally distributed and therefore we will standardise them as follows: 

`z = ( x - μ ) / σ`

Where for each sample, `x` equals the value of the variable, `μ` (_mu_) equals the mean of `x`, and `σ` (_sigma_) equals the standard deviation of `x`.


### MCP1 plaque levels: experiment 2

We will explore the plaque levels. As noted above, we will use `MCP1_pg_ml_2015`, this was experiment 2 in 2015 on the LUMINEX-platform and measurements are in pg/mL.

```{r DataExploration: MCP1 plaque Exp2}

summary(AEDB.CEA$MCP1_pg_ml_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary))
```

```{r DataExploration: MCP1 plaque Exp2 visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())

p1 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p3)
```


### MCP1 plaque levels: experiment 1

We will explore the plaque levels. As noted above, we will use `MCP1`, this was experiment 1 on the LUMINEX-platform and measurements are in pg/mL.

```{r DataExploration: MCP1 plaque Exp1, message=FALSE, warning=FALSE}

# summary(AEDB.CEA$MCP1)
# 
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
# 
attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
detach(AEDB.CEA)

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque Exp1 visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())


AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())

p1 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p3)

```

#### Correlations between MCP1 plaque levels and transformations

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. 

```{r MCP1 scatters: transformations}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 1",
                        ylab = "experiment 2",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

```

## Preliminary conclusion data exploration

Based on the `inverse-rank normal transformation` we conclude there are no outliers and the data approximates a normal distribution. We will apply `inverse-rank normal transformation` on all proteins and focus the analysis on MCP1 in plaque.


# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]
13. Year of surgery [`ORdate_year`] as we discovered in Van Lammeren _et al._ the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

## Models

We will analyze the data through four different models

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque MCP1 levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

### Quantitative traits

We inspect the plaque characteristics, and `inverse-rank normal transformation` continuous phenotypes.

```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed ('unstable') as compared to 'stable'. 

```{r CrossSec: plaques - transformations and visualisations continuous MAC-SMC}

AEDB.CEA$MAC_SMC_ratio <- AEDB.CEA$macmean0 / AEDB.CEA$smcmean0

AEDB.CEA$MAC_SMC_ratio_rank  <- qnorm((rank(AEDB.CEA$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MAC_SMC_ratio)))


cat("Summary of data.\n")
summary(AEDB.CEA$Macrophages_rank)
summary(AEDB.CEA$SMC_rank)
summary(AEDB.CEA$MAC_SMC_ratio_rank)

ggpubr::gghistogram(AEDB.CEA, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())
```


### Binary traits
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages binned
cat("Summary of data.\n")
summary(AEDB.CEA$Macrophages.bin)
contrasts(AEDB.CEA$Macrophages.bin)

AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_binned)) %>%
  group_by(Gender, MAC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages grouped
cat("Summary of data.\n")
AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)
contrasts(AEDB.CEA$macrophages)

AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_grouped)) %>%
  group_by(Gender, MAC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (grouped)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# SMC binned
cat("Summary of data.\n")
summary(AEDB.CEA$SMC.bin)
contrasts(AEDB.CEA$SMC.bin)

AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_binned)) %>%
  group_by(Gender, SMC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)

# SMC grouped
cat("Summary of data.\n")
AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)
contrasts(AEDB.CEA$smc)

AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_grouped)) %>%
  group_by(Gender, SMC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (grouped)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)


# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

### Correlations between MCP1 plaque levels and surgery year

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).

```{r MCP1 scatters: year of surgery}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 2, [pg/mL]",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p2 

rm(p1, p2)

```


In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN.RANK = c("MCP1_pg_ml_2015_rank", "MCP1_rank")

TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "MAC_SMC_ratio_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")


# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as.numeric(year(AEDB.CEA$dateok))

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
summary(AEDB.CEA$ORdate_epoch)

cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
table(AEDB.CEA$ORdate_year)

COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the `inverse-rank normalized` data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque MCP1 levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.


```{r CrossSec: symptoms - logistic regression MODEL1 RANK}
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque MCP1 levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times, message=FALSE, warning=FALSE}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

##### Model 1
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

##### Model 1
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")

write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)


```


#### 90-days follow-up

##### Model 1 
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)


```


# Correlations
We correlated plaque levels of the biomarkers.

## MCP1 plaque levels

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ml_2015_rank",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

```

```{r MCP1 Correlations table}
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

```

```{r MCP1 Correlations alternative visual 1, message=FALSE, warning=FALSE}
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
```


```{r MCP1 Correlations alternative visual 2, message=FALSE, warning=FALSE}
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ml_2015_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```


# Session information

------

    Version:      v1.1.0
    Last update:  2021-02-11
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_
    
    _C_
    
    
    _W_
    
    
    **Changes log**
    * v1.1.0  Fixes needed for compiling the HTML.
    * v1.0.19 Updating for different macOS devices. Addressing reviewer comments.
    * v1.0.18 Changed 'asymptomatic vs. symptomatic' DotPlot to have dots instead of lines. Added boxplot for the same.
    * v1.0.17 Added regular, and per gender boxplots for risk factors, _etc_. Changed coloring for consistency. 
    * v1.0.16 Create a pg/mL-only version. Switched to a new .RMD, but kept versioning. Removed the plasma-based analyses.
    * v1.0.15 Add sex-stratified plots for MCP1 plaque levels by symptoms and plaque vulnerability index.
    * v1.0.14 Add analysis on plasma based MCP1 levels measured through OLINK, n ± 700, limited to symptomatic patients only.
    * v1.0.13 Splitting RMDs into plaque-focused, and one including plasma levels of MCP1.
    * v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
    * v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
    * v1.0.10 Add analyses for all three MCP1, MCP1_pg_ml_2015, and MCP1_pg_ug_2015. Add comparison between MCP1, MCP1_pg_ml_2015, and MCP1_pg_ug_2015. Add (and fixed) ordinal regression. Double checked which measurement to use. 
    * v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
    * v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the MCP1 and MCP1_pg_ug_2015 variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
    * v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions.
    * v1.0.0 Inital version.
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".main_analyses.RData"))
```

------
<sup>&copy; 1979-2021 Sander W. van der Laan | s.w.vanderlaan-2[at]gmail.com | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------


